Familiarity with key abbreviations for incident management KPIs (Key Performance Indicators) is essential for effective performance analysis. In this article, we’ll explore calculating metrics like MTTR and MTBF, compare different metrics, and consider the role of software tools, such as CMMS and EAM systems, in managing and improving metrics like MTBF and MTTR.

Definitions of reliability metrics

What is MTTF?

MTTF stands for mean time to failure. It is the average lifespan of a given device. The mean time to failure is calculated by adding up the lifespans of all the devices and dividing it by their count.

MTTF = total lifespan across devices / # of devices

MTTF is specific to non-repairable devices, like a spinning disk drive; the manufacturer would talk about its lifespan in terms of MTTF. 

For example, consider three dead drives pulled out of a storage array. S.M.A.R.T. indicates that they lasted for 2.1, 2.7, and 2.3 years, respectively.

(2.1 + 2.7 + 2.3) / 3 = ~2.37 years MTTF

We should probably buy some different drives in the future.

MTTF alternatively stands for mean time to fix, but it seems that “failure” is the more common meaning.

Related:

What is MTBF?

MTBF stands for the mean time between failures. MTBF is used to identify the average time between failures of something that can be repaired. 

The mean time between failures is calculated by adding up all the lifespans of devices and dividing by the number of failures:

MTBF = total lifespan across devices / # of failures

The total lifespan does not include the time it takes to repair the device after a failure. 

An example of MTBF would be how long, on average, an operating system stays up between random crashes. 

Related: 

What is MTTR?

MTTR stands for mean time to repair, mean time to recovery, mean time to resolution, mean time to resolve, mean time to restore, or mean time to respond. Mean time to repair and mean time to recovery seem to be the most common. 

The mean time to repair (and restore) is the average time it takes to repair a system once the failure is discovered. It is calculated by adding the total time spent repairing and dividing that by the number of repairs. 

MTTR (repair) = total time spent repairing / # of repairs

For example, let’s say three drives we pulled out of an array, two of which took 5 minutes to walk over and swap out a drive. The third one took 6 minutes because the drive sled was a bit jammed. So:

(5 + 5 + 6) / 3 = 5.3 minutes MTTR

The mean time to repair assumes that the failed system is capable of restoration and does not require replacement. It is synonymous with the mean time to fix. 

Mean time to recovery, resolution, and resolve is the time it takes from when something goes down to when it is back and at full functionality. This includes everything from finding the problem, fixing it, and using technology (like CMMS and EAM systems) to analyze historical data and current assets to develop your maintenance strategy. In DevOps and ITOps, keeping MTTR to an absolute minimum is crucial. 

MTTR (recovery) = total time spent discovery & repairing / # of repairs

The mean time to respond is the most basic of the bunch. The mean time to respond is the average time it takes to respond to a failure. 

Related:

What is MTRS?

MTRS stands for the mean time to restore service. It is the average time it takes from when something that has failed is detected to the time that it is back and at full functionality. MTRS is synonymous with mean time to recovery and is used to differentiate mean time to recovery from mean time to repair. MTRS is the preferred term for mean time to recovery, as it’s more accurate and less confusing, per ITIL v4. 

MTRS = total downtime / # of failures

Let’s take an example of an organization that suffered from four outages. The downtime for each failure is the following:

First, calculate the total downtime experience: 3 + 2 + 4 + 1 = 10 hours

After that, divide the total downtime by the number of outages: 10 / 4 = 2.5 hours

That gives you an MTRS of 2.5 hours, which may need improvement depending on how vital your services are. 

For example, if the service going down is a payment system, whether online payments or in-store payments with a POS, you don’t want those systems down for several hours at a time.

Related: 

What is MTBSI?

MTBSI stands for mean time between service incidents and is used to measure reliability. MTBSI is calculated by adding MTBF and MTRS together.

MTBSI = MTBF + MTRS

Here’s an example of an enterprise’s database server. Over a span of several weeks, you collect the following information:

To calculate your MTBSI, just add those numbers: 300 + 4 = 304 hours

This means your database server will experience an incident, on average, every 304 hours. This metric will help your maintenance team assess your server’s reliability and look for opportunities to improve uptime.

After all, you don’t want critical applications going down too often when your team relies on them being online.

What is MTTD?

MTTD stands for mean time to detect. This is the average time it takes you, or more likely a system, to realize that something has failed. MTTD can be calculated by adding up all the times between failure and detection and dividing them by the number of system failures.

MTTD = total time between failure & detection / # of failures

MTTD can be reduced with a monitoring platform capable of checking everything in an environment. With a monitoring platform like LogicMonitor, MTTD can be reduced to a minute or less by automatically checking everything in your environment for you.

Related:

What is MTTI?

MTTI stands for mean time to identify. Mean time to identify is the average time it takes for you or a system to identify an issue. You can calculate the MTTI by adding the total time from discovering an issue to identifying the solution and dividing that number by the total number of occurrences.

MTTI = total time from issue occurrence to identification / number of issues

For example, let’s look at an example where your organization is responsible for maintaining a web application. Over the course of a month, you identify four instances of poor performance:

Start by calculating the total time to identify your web issues: 35 + 20 + 10 + 15 = 80 minutes

Then divide by the number of issues (80 / 4 = 20) to get 20 minutes as the MTTI to identify issues. For critical applications, you may want to reduce this by adding real-time monitoring to gather data about your IT infrastructure, creating alerts to notify your team about issues that may contribute to an occurrence, and training your team to interpret monitoring data.

Related:

What is MTTK?

MTTK stands for mean time to know. MTTK is the time between when an issue is detected and when the cause of that issue is discovered. In other words, MTTK is the time it takes to figure out why an issue happened. To calculate this, determine the amount of time it takes your team to identify the root cause of problems and divide it by the number of problems encountered.

MTTK = total time from issue detection to root cause identification / number of issues

For example, imagine that your organization maintains critical infrastructure for customers (such as a SaaS service) that they rely on to function. Any downtime will lead to dissatisfaction and a potential loss of revenue.

You measure your MTTK to determine how quickly your team gets services back online. Your team has the following identification times over the course of a month:

You can calculate your MTTK with the following: 1.5 hours + 1.75 hours + 1 hour / 3 incidents = 1.42 MTTK

Knowing this number will help you determine how effective the process your team uses to diagnose processes is. You can then look for areas to optimize to reduce your MTTK.

What is MDT?

MDT stands for mean downtime. It is simply the average period that a system or device is not working. MDT includes scheduled downtime and unscheduled downtime. In some sense, this is the ultimate KPI. The goal is 0. Improving your mean time to recovery will ultimately improve your MDT.

MDT = total downtime / number of events

Let’s take an example of a critical application your IT team supports. Over the course of a month, you experience the following downtimes:

Calculate the MDT by adding the total time and the number of instances: (120 + 30 + 60 + 25) / 4 = 58.75 minutes

Depending on when those downtimes occur, your team may need to look for optimizations to reduce them—or if they are planned downtime, make sure they occur during off hours when demand is reduced.

What is MTTA?

MTTA stands for mean time to acknowledge. It is the average time from when a failure is detected to when work begins on the issue.

MTTA = total time to acknowledge detected failures / # of failures

Imagine the 100-meter dash. The starting horn sounds; you detect it a few milliseconds later. After a few more milliseconds, your brain has acknowledged the horn by making your legs start running. Measure that 100 times, divide by 100, voila, MTTA.

This KPI is particularly important for on-call DevOps engineers and anyone in a support role. DevOps engineers need to keep MTTA low to keep MTTR low and to avoid needless escalations. Support staff needs to keep MTTA low to keep customers happy. Even if you’re still working toward a resolution, customers want to know their issues are acknowledged and worked on promptly.

Related: 

What is MTTV?

MTTV stands for the mean time to verify. Mean time to verify is typically the last step in mean time to restore services, with the average time from when a fix is implemented to having that fix verified that it is working and has solved the issue.

MTTV = total time to verify resolution / # of resolved failures

You can improve this KPI in your organization by automating verification through unit tests at the code level or with your monitoring platform at the infrastructure, application, or service level.

Metric comparisons

MTTR vs MTBF

MTBF (Mean Time Between Failures) measures the time a system operates before failure, indicating its reliability and helping plan maintenance schedules. MTTR (Mean Time to Repair) measures the time it takes to repair a system after failure, focusing on minimizing downtime and repair costs. Simply put, MTBF evaluates reliability, while MTTR measures repair efficiency.

Calculating MTTR and MTBF

Let’s say an IT team manages several servers with a total number of 10 assets. During that time:

Starting with MTBF, start with the total number of operational hours and divide it by the number of failures: 7,200 / 5 = 1,400 hours

This means you have an average of 1,400 hours of uptime before you experience a server failure that leads to unscheduled downtime.

Calculating MTTR, on the other hand, tells you how well your team handles repairs and how quickly they get the servers back online.

To calculate this, take the total repair time and divide it by the number of repairs: 15 hours / 5 repairs = 3 hours

These calculations will help you understand how often downtime occurs, how long it takes to bring services online, and how often you can expect it to happen each month. 

Improving MTTR and MTBF

These calculations will also help your team improve maintenance schedules to address these problems, reducing total downtime and the number of incidents. Predictive and preventative maintenance strategies can be implemented to catch potential issues before they become major problems, increasing MTBF and decreasing MTTR.

Implementing redundancies and fault tolerance measures can also greatly improve both MTBF and MTTR. By having backup systems in place, downtime due to hardware failures can be minimized or even eliminated. 

MTTF vs MTBF

The main difference between MTTF and MTBF is how each is resolved, depending on what failure happened. In MTTF, what is broken is replaced, and in MTBF, what is broken is repaired. 

MTTF and MTBF even follow the wording naturally. “To failure” implies it ends there, while “between failures” implies there can be more than one.

In many practical situations, you can use MTTF and MTBF interchangeably. Lots of other people do.

The remedy for hardware failures is generally replacement. Even if you’re repairing a problematic switch, you’re likely replacing a failed part. Something like an operating system crash still requires something that could be considered a “repair” instead of a “replacement.”

MTTF and MTBF are largely the concerns of vendors and manufacturers. You can’t change the MTTF on a drive, but you can run them in a RAID and drive down MTTR for issues within your infrastructure

You generally can’t directly change your hardware’s MTTF or MTBF. Still, you can use quality components, best practices, and redundancy to reduce the impact of failures and increase the overall service’s MTBF.

MTTD vs MTTI

The mean time to detect and the mean time to identify are mostly interchangeable, depending on your company and the context. 

MTTD vs MTTA

Detecting and acknowledging incidents and failures are similar but often differentiate themselves in the human element. MTTD is most often a computed metric that platforms should tell you.

For instance, in the case of LogicMonitor, MTTD would be the average time from when a failure happened to when the LogicMonitor platform identified the failure. 

MTTA takes this and adds a human layer, taking MTTD and having a human acknowledge that something has failed. 

MTTA is important because while the algorithms that detect anomalies and issues are incredibly accurate, they are still the result of a machine-learned algorithm. A human should make sure that the detected issue is indeed an issue. 

MTTF (failure) vs MTTR: Mean time to failure vs Mean time to repair

Mean time to failure typically measures the time in relation to a failure. Mean time to repair measures how long it takes to get a system back up and running. This makes for an unfair comparison, as what is measured is very different.

Let’s take cars as an example. Let’s say your 2006 Honda CR-V gets into an accident. MTTF could be calculated as the time from when the accident occurred to the time you got a new car. MTTR would be the time from when the accident occurred to when the car was repaired. 

MTTF (fix) vs MTTR: Mean time to fix vs mean time to repair

Mean time to fix and mean time to repair can be used interchangeably. The preferred term in most environments is mean time to repair.

MTRS vs MTTR: Mean time to restore service vs mean time to repair

The mean time to restore service is similar to the mean time to repair service, but instead of using the time from failure to resolution, it only covers the time from when the repairs start to when full functionality is restored.

In general, MTTR as a KPI is only so useful. It will tell you about your repair process and its efficiency, but it won’t tell you how much your users might be suffering. If it takes 3 months to find the broken drives, and they are slowing down the system for your users, 5.3 minutes MTTR is not useful or impressive.

Typically, customers care about the total time devices are down much more than the repair time. They want to be down as little as possible. For the sake of completeness, let’s calculate this one too:

((5 + 5 + 6) + ( 3 + 3 + 3) ) / 3 = 8.3 minutes MTTR

In general, the MTTR KPIs will be more useful to you as an IT operator. 

The role of CMMS and EAM systems in managing reliability metrics

Computerized Maintenance Management Systems (CMMS) and Enterprise Asset Management (EAM) software are essential tools available to your team that will help them track reliability and failure metrics. They offer many features that help, including:

These tools will help your organization move from a reactive approach to a proactive one, where you stay ahead of problems and minimize downtime.

From ambiguity to action: Defining KPIs for better outcomes

When an incident occurs, time is of the essence. These KPIs, like MTTF, MTTD, MTTR, and MTBF, can help you gain better insight into your remediation processes and find areas to optimize. 

Unfortunately, because each KPI has subtle similarities, many meanings differ from company to company. For example, MTTF and MTBF both tell you how long you can expect a device to stay online before failing, but MTTF is often used to identify how long a device takes to break (instead of offline for repair). 

If these initialisms come up in a meeting, I suggest clarifying the meaning with the speaker—eventually solidifying these definitions in your organizations to avoid confusion. Otherwise, you might be DOA.

Over the past 25 years, I’ve been privileged to help businesses navigate some of the most significant shifts in technology. At Salesforce, I saw the cloud revolutionize how businesses adopt and scale software. At Slack, we reimagined collaboration by bringing connection and emotion into the workplace.

Today, at LogicMonitor, I see a similar inflection point. Generative AI is poised to transform industries, but its success hinges on the infrastructure beneath it. The recent $800 million investment of equity and strategic financing from leading investment firms like like PSG, Golub Capital, as well as the continued support of Vista Equity Partners is a clear signal of the commitment to helping businesses unlock the full potential of AI and data center technologies—empowering them to work smarter, faster, and more responsibly.

My conversations with CIOs globally highlight one universal truth: businesses need systems they can trust. My career has always been about bridging technology and business outcomes, and LogicMonitor is the culmination of that mission.

What this $800 million investment means for LogicMonitor

With this $800 million investment, we’re driving progress in three critical areas:

As AI reshapes industries, we’re dedicated to ensuring businesses stay ahead—empowered to innovate, adapt, and lead with confidence.

Our customers make real-world impact

For industries like healthcare, where AI is driving personalized medicine, or manufacturing, where robotics and machine learning are revolutionizing operations, infrastructure reliability is key. Without it, these advances fall flat.

LogicMonitor empowers organizations to bridge this gap, ensuring that their systems are as resilient as their vision is ambitious. 

The results speak for themselves. Our customers consistently realize incredible value from our platform and these stories are proof that businesses can thrive when equipped with the right tools and insights.

Together, we can shape the future

LogicMonitor isn’t just watching the future unfold—we’re shaping it. 

We’re proving that businesses can reduce energy use, cut costs, and drive innovation simultaneously. We help optimize cooling systems, improve energy efficiency, and reduce waste—all by enabling organizations with AI-driven insights. Sustainability isn’t just a goal for us. It’s a responsibility we share with every customer we support.

With the right tools, a bold vision, and a commitment to doing things responsibly, we can create a smarter, more sustainable future together.

And if you’re not on the winning team yet, I invite you to see us in action before we get to the championship game and take the gold.

Organizations of all sizes have a complex array of hardware, software, staff, and vendors. Each of those assets comes with complex configurations and relationships between them. Visualizing and tracking these configurations and relationships over time is critical to quickly responding to incidents. Plus, it helps inform business decisions, especially regarding future IT components and upgrades. 

Any organization familiar with the ITIL framework will know the term configuration management database (CMDB). This unique database aims to track a company’s assets and all of the complex relationships between them. However, designing a configuration management database is not that easy. You must consider what to include, how to find it, the intricacies of maintaining it, and everything in between.

Are you interested in implementing a configuration management database in your IT department, or need help improving a CMDB project gone wrong? If so, this guide will help you find a feasible solution to maintain and make it accessible to everyone who needs it.

What is a CMDB?

A configuration management database (CMDB) is unlike other databases because it’s designed entirely for internal management and control purposes. A CMDB acts as a central repository. It’s used to track and control the relationships between various IT assets and their established configurations. For any company implementing the Information Technology Infrastructure Library (ITIL) framework, a CMDB is crucial to IT processes. 

The ITIL framework lays out many crucial IT standards and processes. These pertain to incident response, availability, deployment management, and other key activities. The framework makes suggestions to help better align these IT activities with business objectives. Doing so recognizes that the most up-to-date and accurate information must inform these processes and the resulting decisions. So, to execute the framework, IT departments require good configuration management. That means enlisting the help of a CMDB. 

Configuration management aims to give a team the context it needs to evaluate an asset. Instead of viewing it in a silo, the IT department can look at the CMDB to see how it relates to other assets. They can then see how changing its configuration will impact the organization. This information allows IT managers and administrators to make better-informed decisions. Thus, a CMDB helps plan releases, deploy new components, and respond to incidents. 

For example, if something disrupts the business’s network and impacts all workstations in a given department, an IT administrator would have difficulty manually tracking down the routers and servers involved in the issue. This would lead to a great deal of trial and error or information hunting just to start step one of resolving the issue. On the other hand, if that administrator has a CMDB to reference, they can immediately figure out the routers, servers, and other infrastructure involved. 

Even with a basic example such as this, it’s clear to see that a CMDB is incredibly valuable for IT professionals. CMDBs will take time to set up and maintain. However, their ability to speed up incident resolution, simplify deployments, and better inform IT decisions means the investment will pay off rapidly. 

Why is a CMDB important?

The role of a CMDB in the IT department is clear. With all of the information in front of them, an IT professional can better make decisions pertaining to incident resolution, system updates, and infrastructure upgrades. The result is more efficient resource utilization and less trial-and-error. In turn, that helps the entire organization continue running smoothly.

In addition to giving IT insight into how an organization’s data assets are being controlled and connected, a CMDB also reveals data that are siloed in various departments. This information helps organizations restore accessibility and visibility at scale. A CMDB improves data governance. In turn, that helps support the mission-critical activities of the company’s planners, accountants, and operations staff.

As you can see, the CMDB’s role has a far-reaching impact that ultimately touches every facet of an organization. A lack of visibility will directly impact operations, compliance, and reporting. That’s why implementing CMDB helps businesses overcome inefficiencies.  

How do CMDBs work?

CMDBs work by gathering data from different sources and storing information about IT assets and other configuration items in a commonplace that is easily accessible. Even for a small company, CMDBs are necessary. Once an IT department begins analyzing all of its assets and the complicated relationships between them, it will discover a substantial amount of information that must be stored. Plus, that information needs to be updated often.

Using a CMDB is regarded as the most efficient way to store IT information. After all, it can track complicated configurations, relationships, and dependencies with ease. When designing a CMDB, you should plan to enter all known assets. These assets are referred to as “configuration items” (CIs). Once all assets are entered, it is then the responsibility of the IT department to connect the dots. That means defining the various relationships between the CIs. 

There are several assets that a department may need to track. Some examples include hardware, software, documentation, and vendors. Both manual and automated tools exist to help IT departments discover their assets and the relationships between them. While it’s not possible to achieve and maintain complete accuracy, departments should strive to keep the CMDB as up-to-date as possible. If it’s not updated, the CMDB won’t be able to serve its purpose effectively.

Regarding who should be in charge of creating the CMDB, it’s a group effort. Once the CIs have been identified, their respective owners should be brought into the process as early as possible. These individuals will hold helpful knowledge about the asset and its complex relationships. The involvement of these stakeholders helps to make sure that the CMDB is accurate and complete. 

Once data has been brought into the CMDB, the challenge becomes maintaining it. Certain characteristics set a good, usable CMDB apart from those ultimately not maintained. Failing to prioritize these characteristics could mean the CMDB is eventually abandoned due to inefficiencies and resource consumption. 

Real-time monitoring and incident management with a CMDB

A CMDB plays a pivotal role in real-time monitoring by providing IT teams with a centralized view of all CIs and their relationships. When integrated with monitoring and automation tools, a CMDB can continuously track the health of critical IT assets, proactively alerting teams to potential issues and reducing the time to resolution.

For instance, suppose a network disruption impacts multiple assets. A CMDB enables IT administrators to quickly identify the affected routers, servers, and applications by viewing dependency mappings. This view accelerates root cause analysis, allowing teams to isolate the issue and focus on specific CIs instead of manually troubleshooting a broad network.

Additionally, CMDBs integrated with AIOps can automatically trigger responses for certain incidents. This functionality allows IT teams to automate routine responses, freeing up resources to focus on high-priority tasks. With automated workflows, the CMDB can also log incident data, providing an audit trail that supports compliance and continuous improvement.

Incorporating real-time monitoring through a CMDB thus enhances an IT department’s ability to manage incidents effectively and maintain system stability, ultimately minimizing downtime and improving service reliability.

Characteristics of a CMDB  

Now, you have a big-picture understanding of how a CMDB works and the role it plays in IT and the ITIL framework. However, it’s also important to approach it in a more practical sense. A CMDB may store hundreds, if not thousands, of CIs. How are these discovered, maintained, and utilized on a day-to-day basis? That depends on the exact features and characteristics of the CMDB you’re designing.

The first characteristics that need to be identified relate to the creation and maintenance of the database itself. Departments will need to pull in data manually and with API-driven integrations. There should also be automation involved. Without automated discovery, accurately creating and maintaining the CMDB will prove challenging. So, incorporating scanning tools into the CMDB should be a top priority.  

During the creation and throughout its use, the department needs to maintain a graphical representation of all the CIs in the database. You should be able to see how CIs are dependent on each other at a glance. This is known as service mapping. Some CMDB tools can generate a service map automatically.

By providing a clear, visual representation of these dependencies, service mapping helps IT teams quickly understand the relationships between configuration items (CIs). This level of visibility is critical when planning changes, as it allows teams to assess the potential impact on interconnected systems before implementation. For example, if a critical server is scheduled for an update, service mapping instantly shows which applications and services depend on that server. This insight minimizes the risk of unforeseen IT outages, allowing for smoother change management.

Once established, a CMDB should be intuitive, accessible, and visual whenever possible. This starts by implementing dashboards that track specific metrics about the CIs and their relationships. For instance, IT departments should be able to pinpoint how a change or release impacts the health of relevant CIs. The dashboard should also reveal patterns in incident reports, outstanding issues, and associated costs. 

The IT department should also have visibility into compliance, especially when working at the enterprise level. Auditors need to know the state of CIs and have access to historical incidents and changes. For that reason, transparency and reporting are critical characteristics of a CMDB.

Enabling users to gain access to the database is critical, limiting what they can view and change. For that reason, access controls are another essential characteristic. A lack of access controls will lead to significant data integrity and compliance challenges.  

Implementing a CMDB presents several additional challenges, including cultural differences, relevance, centralization, accuracy, processes, and tool-related issues. These obstacles can complicate the implementation process and impede the realization of its full benefits.

As you can see, the design of a CMDB can grow very complicated very fast. This is why the IT department must gather key stakeholders. Teams must discuss the organization’s compliance needs and other considerations before they implement a CMDB. With a well-informed team in place, a business is empowered to design underlying infrastructure that’s feasible to maintain and use daily. 

Once established, a CMDB should be intuitive, accessible, and visual whenever possible. Dashboards tracking metrics about CIs and their relationships can enhance team-wide understanding of the IT infrastructure. These visual aids not only help pinpoint the impact of changes but also improve cross-departmental communication by presenting complex dependencies in a user-friendly way.

Should you implement CMDB monitoring?

Implementing a CMDB helps organizations manage their assets and regain visibility into their data and infrastructure. Any organization following the ITIL framework needs a CMDB. However, smaller companies may feel that they will not be able to realize great value from one. 

In truth, companies of all sizes—including small businesses—are finding that a CMDB is becoming more important. No matter the size of your operations, you are not exempt from complying with data privacy and protection regulations. As data governance standards grow more strict, visibility is crucial.  

In addition, a CMDB helps companies improve the observability of their systems. Even smaller companies struggle as data and assets become more distributed across the cloud, on-premises, and third-party applications. With all that in mind, a CMDB is likely a worthy investment for your business. 

The good news is that you do not have to build your CMDB from scratch. Several solution providers can help your company establish a CMDB. They even have the associated dashboards, tracking, and access controls. The result is a CMDB that’s easier to implement, use, and maintain. Achieving that reality takes the right partners.

CMDB implementation and maintenance tips

Implementing a CMDB may seem daunting, but starting with a focused approach can set the foundation for long-term success. Begin with the critical assets and CIs that have the most impact on daily operations. By establishing the core components first, your team can gradually expand the CMDB’s scope over time as more data and resources become available.

Involve key stakeholders early in the process to ensure accuracy and completeness. Each department typically has unique insights into its assets, making their involvement essential for identifying dependencies and asset relationships accurately. These stakeholders not only provide valuable information during the setup but also contribute to ongoing maintenance.

Automation is a cornerstone of effective CMDB management, particularly as IT environments grow in complexity. Automated discovery tools help keep asset information up-to-date, reducing the risk of outdated or incomplete data. When combined with regular review schedules, automation ensures that the CMDB remains a reliable source of truth for configuration and asset management.

IT Asset Management (ITAM) also plays a crucial role in managing IT infrastructure. While CMDB focuses on the technical details and relationships between assets, ITAM focuses on ensuring all assets are accounted for and managed efficiently throughout their lifecycle. This includes procurement, deployment, maintenance, and retirement of assets.

Finally, schedule periodic audits of the CMDB to maintain data accuracy and relevance. Regular reviews help identify and address discrepancies, ensuring the database remains useful for incident response, change management, and compliance.

Ready to take your CMDB to the next level? 

A well-maintained CMDB goes beyond simple configuration tracking—it becomes an operational backbone that empowers IT departments to align with business objectives, enhance compliance, and improve infrastructure planning. By centralizing and visualizing the relationships between critical assets, a CMDB enables IT teams to make informed, strategic decisions that support both immediate and long-term business goals.

Whether you’re a small business looking to establish a CMDB or a large organization ready to optimize an existing setup, now is the time to assess your needs and start planning for growth. Implementing and maintaining a CMDB is an investment in your organization’s future—ensuring visibility, accountability, and agility as IT environments continue to evolve.

The LogicMonitor ServiceNow CMDB Integration eliminates the need for time-consuming data sifting across systems, so you can gain a holistic view of your ecosystem, from infrastructure to applications. With immediate notifications for changes, you can stay on top of your assets and their dependencies without missing a beat.

Logging as a Service (LaaS) provides a cloud-based, centralized solution for managing log data from applications, servers, and devices, helping IT teams streamline analysis, improve security, and reduce infrastructure costs. By automating data collection and offering scalable storage, LaaS enables fast, efficient insights into system performance and security across diverse environments.

LaaS allows companies to manage log data regardless of whether it comes from applications, servers, or devices. With LaaS, companies can more easily aggregate and collate data, scale and manage storage requirements, set up notifications and alerts, and analyze data and trends. It also allows teams to customize dashboards, reports, and visualizations. It’s a platform designed to accommodate a company’s requirements for scalability.

Unlike traditional log management models, LaaS provides flexible, cloud-based services and support, enabling teams to access data centers instantly through web-based configurations. This agility not only supports real-time decision-making but also helps organizations optimize resources, enhance security, and ensure compliance—all without the infrastructure costs or maintenance burden of traditional solutions.

Why is log management so challenging?

A conventional approach to log management has its drawbacks. Here are a few of the challenges you might face with log management:

Resource heavy

Log management tools require significant storage and consume CPU time. Not only do they need substantial storage for log data, but they also eat up considerable CPU time collecting, indexing, and managing logs in real time. As a result, you may have fewer resources for locally running tools and applications, slowing down operations or impacting system performance. With LaaS, cloud-based resources scale automatically, allowing log data to be processed without consuming critical on-premises resources.

Poor reliability

Traditional log management tools can crash when they are locally hosted, or hosting servers might fail, especially during high-traffic periods or data surges. This lack of reliability means you could lose valuable log data, impacting your ability to investigate incidents or maintain compliance—a risk no organization can afford. LaaS, by operating in the cloud, offers high availability and built-in redundancy to prevent data loss and ensure continuous log access.

Minimal support

With logging tools, the scenario lends itself to a potentially faulty situation if you’re running more than a few applications across servers. Some companies may run some apps locally while running others in the cloud, creating data silos that complicate log collection and correlation. LaaS simplifies log management for hybrid environments, consolidating logs across on-premises and cloud systems for a unified view, which is critical for complex, multi-cloud infrastructures.

Lack of scalability

For companies relying on a local log retention solution, the host infrastructure’s size may cause challenges. To increase log volume for your company, you’ll probably need access to more resources, which may already be limited, resulting in performance bottlenecks and potential data loss. Scaling up an on-premises solution often requires substantial hardware investments, making it costly and time-consuming. LaaS solutions, on the other hand, scale effortlessly in the cloud, handling increased log volumes without requiring additional infrastructure investment. 

High costs and maintenance overheads

Traditional log management solutions often require significant upfront investment in hardware and incur ongoing maintenance costs, as IT teams must regularly update software, manage storage, and troubleshoot issues. With LaaS, these maintenance burdens are significantly reduced, as the service provider handles updates, storage management, and troubleshooting, freeing up your IT team for more strategic tasks.

What are the benefits of Logging as a Service?

Logging as a Service maintains records of how the software is used for specific roles and functions across organizations. As a highly scalable solution, LaaS allows companies to focus on analyzing and better understanding the data logs instead of maintaining them. 

Ensures greater efficiency

Logging as a Service deploys an enterprise-wide solution quickly and reliably. LaaS solutions ensure innovation with centralized deployment and updates. Outsourcing software management lends greater productivity to teams across the organization. With fast and reliable tracking LaaS options, you can correlate events and perform distributed traces across software environments. For example, e-commerce platforms benefit by quickly correlating user behaviors and system events to improve user experience and minimize downtime during peak shopping periods.

Protects your company

With better security measures, Logging as a Service can configure your infrastructure and log files to better support your company’s needs. LaaS monitors your systems for data breaches to give you better control and protection while supporting regulatory compliance as part of LaaS services. This means the service can quickly and reliably roll out security hotfixes. In industries like healthcare, where patient data privacy is critical, LaaS helps ensure data security and HIPAA compliance by monitoring logs for access control and vulnerabilities.

Visualizes log data

Visualizations are valuable and important. With graphing and data plotting tools used in a LaaS solution, companies can more easily visualize log data without the installation and configuration requirements. Companies save time and money and reduce frustration by avoiding manually importing data. In finance, LaaS visualization tools enable teams to monitor transactions in real-time, allowing for quicker detection of anomalies that could indicate fraud.

Adapts to changing requirements

LaaS services support better adaptability to IT environments. With the realities of cloud infrastructure, container management, and even remote work scenarios, your company and team must be agile. For instance, companies in the tech industry frequently change or scale their cloud infrastructure, and LaaS seamlessly supports this adaptability, ensuring consistent cloud-based log management without reconfigurations.

Accounts for global coverage

Evolutions in IT environments and requirements mean that you should consider global coverage opportunities. LaaS services support regulatory enforcement and intellectual property (IP) protection even if your users are spread across the globe.

Delivers 24/7 support 

LaaS services address immediate troubleshooting needs, installing extensions, or reconfiguring logging tools. LaaS supports your need to ensure the functionality and accessibility necessary for operations, and it takes the guesswork out of monitoring those log files. This 24/7 support is particularly valuable for multinational organizations that require constant monitoring to accommodate various time zones and global operations.

LaaS implementation steps

Implementing Logging as a Service is a streamlined process that enables your organization to centralize, monitor, and scale log data effectively. Here’s how to get started:

  1. Assess current logging needs
    Begin by evaluating your organization’s logging requirements. Identify data sources, log volume expectations, compliance needs, and key performance metrics. This assessment helps ensure that the LaaS solution meets your specific use cases and scalability goals.
  2. Select a LaaS provider
    Choose a LaaS provider that aligns with your technical requirements and budget. Consider factors such as scalability, ease of integration, customization options, and customer support. Selecting the right provider is essential for smooth integration and long-term support.
  3. Plan for integration with existing systems
    Map out how LaaS will integrate with your current IT log infrastructure. Identify the applications, servers, and devices that will feed data into the LaaS platform. This planning helps prevent data silos and ensures comprehensive log coverage across your environment.
  4. Configure alerts and dashboards
    Set up custom alerts and dashboards based on your team’s operational priorities. Configure notifications for critical events, such as security breaches or performance anomalies, to ensure proactive log monitoring. Custom dashboards enable faster decision-making by presenting data in an accessible, real-time format.
  5. Test the LaaS setup
    Conduct a thorough test of the LaaS system before full deployment. Check data flow from all intended sources, validate alert accuracy, and ensure that dashboards reflect live data. Testing provides confidence that the setup will perform as expected when handling production-level log volumes.
  6. Monitor and optimize over time
    Regularly review your LaaS setup to refine configurations and add new data sources as your environment evolves. Continuous optimization enables your LaaS solution to scale effectively with your organization, supporting long-term operational goals and maintaining system performance.

Built for the future with LogicMonitor

Logging as a Service is a powerful, future-ready solution designed to meet the evolving needs of modern enterprises. With its scalability, real-time log analytics, and robust security, LaaS enables organizations to stay agile, reduce infrastructure strain, and gain deeper insights into their data. By shifting log management to a cloud-based model, companies can better manage growing data volumes, simplify compliance, and focus more on innovation and less on maintenance.

At LogicMonitor, we’re committed to helping companies transform the way they manage log data, delivering extraordinary employee and customer experiences while maximizing efficiency.

Amazon Web Services (AWS) dominates the cloud computing industry with over 200 services, including AI and SaaS. In fact, according to Statista, AWS accounted for 32% of cloud spending in Q3 2022, surpassing the combined spending on Microsoft Azure, Google Cloud, and other providers.

A virtual private cloud (VPC) is one of AWS‘ most popular solutions. It offers a secure private virtual cloud that you can customize to meet your specific virtualization needs. This allows you to have complete control over your virtual networking environment.

Let’s dive deeper into AWS VPC, including its definition, components, features, benefits, and use cases.

What is a virtual private cloud?

A virtual private cloud refers to a private cloud computing environment within a public cloud. It provides exclusive cloud infrastructure for your business, eliminating the need to share resources with others. This arrangement enhances data transfer security and gives you full control over your infrastructure.

When you choose a virtual private cloud vendor like AWS, they handle all the necessary infrastructure for your private cloud. This means you don’t have to purchase equipment, install software, or hire additional team members. The vendor takes care of these responsibilities for you.

AWS VPC allows you to store data, launch applications, and manage workloads within an isolated virtualized environment. It’s like having your very own private section in the AWS Cloud that is completely separate from other virtual clouds.

AWS private cloud components

AWS VPC is made up of several essential components:

Subnetworks

Subnetworks, also known as subnets, are the individual IP addresses that comprise a virtual private cloud. AWS VPC offers both public subnets, which allow resources to access the internet, and private subnets, which do not require internet access.

Network access control lists

Network access control lists (Network ACLs) enhance the security of public and private subnets within AWS VPC. They contain rules that regulate inbound and outbound traffic at the subnet level. While AWS VPC has a default network NACL, you can also create a custom one and assign it to a subnet.

Security groups

Security groups further bolster the security of subnets in AWS VPC. They control the flow of traffic to and from various resources. For example, you can have a security group specifically for an AWS EC2 instance to manage its traffic.

Internet gateways

An internet gateway allows your virtual private cloud resources that have public IP addresses to access internet and cloud services. These gateways are redundant, horizontally scalable, and highly available.

Virtual private gateways

AWS defines a private gateway as “the VPN endpoint on the Amazon side of your Site-to-Site VPN connection that can be attached to a single VPC.” It facilitates the termination of a VPN connection from your on-premises environment.

Route tables

Route tables contain rules, known as “routes,” that dictate the flow of network traffic between gateways and subnets.

In addition to the above components, AWS VPC also includes peering connections, NAT gateways, egress-only internet gateways, and VPC endpoints. AWS provides comprehensive documentation on all these components to help you set up and maintain your AWS VPC environment.

AWS VPC features

AWS VPC offers a range of features to optimize your network connectivity and IP address management:

Network connectivity options

AWS VPC provides various options for connecting your environment to remote networks. For instance, you can integrate your internal networks into the AWS Cloud. Connectivity options include AWS Site-to-Site VPN, AWS Transit Gateway + AWS Site-to-Site VPN, AWS Direct Connect + AWS Transit Gateway, and AWS Transit Gateway + SD-WAN solutions.

Customize IP address ranges

You can specify the IP address ranges to assign private IPs to resources within AWS VPC. This allows you to easily identify devices within a subnet.

Network segmentation

AWS supports network segmentation, which involves dividing your network into isolated segments. You can create multiple segments within your network and allocate a dedicated routing domain to each segment.

Elastic IP addresses

Elastic IP addresses in AWS VPC help mitigate the impact of software failures or instance issues by automatically remapping the address to another instance within your account.

VPC peering

VPC peering connections establish network connections between two virtual private clouds, enabling routing through private IPs as if they were in the same network. You can create peering connections between your own virtual private clouds or with private clouds belonging to other AWS accounts.

AWS VPC benefits

There are several benefits to using AWS VPC:

Increased security

AWS VPC employs protocols like logical isolation to ensure the security of your virtual private cloud. The AWS cloud also offers additional security features, including infrastructure security, identity and access management, and compliance validation. AWS meets security requirements for most organizations and supports 98 compliance certifications and security standards, more than any other cloud computing provider.

Scalability

One of the major advantages of using AWS VPC is its scalability. With traditional on-premise infrastructure, businesses often have to invest in expensive hardware and equipment to meet their growing needs. This can be a time-consuming and costly process. However, with AWS VPC, businesses can easily scale their resources up or down as needed, without purchasing any additional hardware. This allows for more flexibility and cost-effectiveness in managing resources.

AWS also offers automatic scaling, which allows you to adjust resources dynamically based on demand, reducing costs and improving efficiency.

Flexibility

AWS VPC offers high flexibility, enabling you to customize your virtual private cloud according to your specific requirements. You can enhance visibility into traffic and network dependencies with flow logs, and ensure your network complies with security requirements using the Network Access Analyzer VPC monitoring feature. AWS VPC provides numerous capabilities to personalize your virtual private cloud experience.

Pay-as-you-go pricing

With AWS VPC, you only pay for the resources you use, including data transfers. You can request a cost estimate from AWS to determine the pricing for your business.

Comparison: AWS VPC vs. other cloud providers’ VPC solutions

When evaluating virtual private cloud solutions, understanding how AWS VPC compares to competitors like Azure Virtual Network and Google Cloud VPC is essential. Each platform offers unique features, but AWS VPC stands out in several critical areas, making it a preferred choice for many businesses.

AWS VPS

AWS VPC excels in service integration, seamlessly connecting with over 200 AWS services such as EC2, S3, Lambda, and RDS. This extensive ecosystem allows businesses to create and manage highly scalable, multi-tier applications with ease. AWS VPC leads the industry in compliance certifications, meeting 98 security standards and regulations, including HIPAA, GDPR, and FedRAMP. This makes it particularly suitable for organizations in regulated industries such as healthcare, finance, and government.

Azure Virtual Network

By comparison, Azure Virtual Network is tightly integrated with Microsoft’s ecosystem, including Azure Active Directory and Office 365. This makes it a strong contender for enterprises that already rely heavily on Microsoft tools. However, Azure’s service portfolio is smaller than AWS’s, and its networking options may not offer the same level of flexibility.

Google Cloud VPC

Google Cloud VPC is designed with a globally distributed network architecture, allowing users to connect resources across regions without additional configuration. This makes it an excellent choice for businesses requiring low-latency global connectivity. However, Google Cloud’s smaller service ecosystem and fewer compliance certifications may limit its appeal for organizations with stringent regulatory needs or diverse application requirements.

AWS VPC shines in scenarios where large-scale, multi-tier applications need to be deployed quickly and efficiently. It is also the better choice for businesses with strict compliance requirements, as its security measures and certifications are unmatched. Furthermore, its advanced networking features, including customizable IP ranges, elastic IPs, and detailed monitoring tools like flow logs, make AWS VPC ideal for organizations seeking a highly flexible and secure cloud environment.

AWS VPC use cases

Businesses utilize AWS VPC for various purposes. Here are some popular use cases:

Host multi-tier web apps

AWS VPC is an ideal choice for hosting web applications that consist of multiple tiers. You can harness the power of other AWS services to add functionality to your apps and deliver them to users.

Host websites and databases together

With AWS VPC, you can simultaneously host a public-facing website and a private database within the same virtual private cloud. This eliminates the need for separate VPCs.

Disaster recovery

AWS VPC enables network replication, ensuring access to your data in the event of a cyberattack or data breach. This enhances business continuity and minimizes downtime.

Beyond basic data replication, AWS VPC can enhance disaster recovery strategies by integrating with AWS Backup and AWS Storage Gateway. These services ensure faster recovery times and robust data integrity, allowing organizations to maintain operations with minimal impact during outages or breaches.

Hybrid cloud architectures

AWS VPC supports hybrid cloud setups, enabling businesses to seamlessly integrate their on-premises infrastructure with AWS. This allows organizations to extend their existing environments to the cloud, ensuring smooth operations during migrations or when scaling workloads dynamically. For example, you can use AWS Direct Connect to establish private, low-latency connections between your VPC and your data center.

DevOps and continuous integration/continuous deployment (CI/CD)

AWS VPC provides a secure and isolated environment for implementing DevOps workflows. By integrating VPC with tools like AWS CodePipeline, CodeBuild, and CodeDeploy, businesses can run CI/CD pipelines while ensuring the security and reliability of their applications. This setup is particularly valuable for teams managing frequent updates or deploying multiple application versions in parallel.

Secure data analytics and machine learning

AWS VPC can host secure environments for running data analytics and machine learning workflows. By leveraging services like Amazon SageMaker or AWS Glue within a VPC, businesses can process sensitive data without exposing it to public networks. This setup is ideal for organizations in sectors like finance and healthcare, where data privacy is critical.

AWS VPC deployment recommendations

Deploying an AWS VPC effectively requires following best practices to optimize performance, enhance security, and ensure scalability. Here are some updated recommendations:

1. Use security groups to restrict unauthorized access

2. Implement multiple layers of security

3. Leverage VPC peering for efficient communication

4. Use VPN or AWS direct connect for hybrid cloud connectivity

5. Plan subnets for scalability and efficiency

6. Enable VPC flow logs for monitoring

7. Optimize costs with NAT gateways

8. Use elastic load balancing for high availability

9. Automate deployment with Infrastructure as Code (IaC)

10. Apply tagging for better resource management

By following these best practices, businesses can ensure that their AWS VPC deployments are secure, scalable, and optimized for performance. This approach also lays the groundwork for effectively managing more complex cloud architectures in the future.

Why choose AWS VPC?

AWS VPC offers a secure and customizable virtual private cloud solution for your business. Its features include VPC peering, network segmentation, flexibility, and enhanced security measures. Whether you wish to host multi-tier applications, improve disaster recovery capabilities, or achieve business continuity, investing in AWS VPC can bring significant benefits. Remember to follow the deployment recommendations provided above to maximize the value of this technology.

To maximize the value of your AWS VPC deployment, it’s essential to monitor and manage your cloud infrastructure effectively. LogicMonitor’s platform seamlessly integrates with AWS, offering advanced AWS monitoring capabilities that provide real-time visibility into your VPC and other AWS resources. 

With LogicMonitor, you can proactively identify and resolve performance issues, optimize your infrastructure, and ensure that your AWS environment aligns with your business goals.

At LogicMonitor, we manage vast quantities of time series data, processing billions of metrics, events, and configurations daily. As part of our transition from a monolithic architecture to microservices, we chose Quarkus—a Kubernetes-native Java stack—for its efficiency and scalability. Built with the best-of-breed Java libraries and standards, Quarkus is designed to work seamlessly with OpenJDK HotSpot and GraalVM.

To monitor our microservices effectively, we integrated Micrometer, a vendor-agnostic metrics instrumentation library for JVM-based applications. Micrometer simplifies the collection of both JVM and custom metrics, helping maximize portability and streamline performance monitoring across our services.

In this guide, we’ll show you how to integrate Quarkus with Micrometer metrics, offering practical steps, code examples, and best practices. Whether you’re troubleshooting performance issues or evaluating these tools for your architecture, this article will help you set up effective microservice monitoring.

How Quarkus and Micrometer work together

Quarkus offers a dedicated extension that simplifies the integration of Micrometer, making it easier to collect both JVM and custom metrics. This extension allows you to quickly expose application metrics through representational state transfer (REST) endpoints, enabling real-time monitoring of everything from Java Virtual Machine (JVM) performance to specific microservice metrics. By streamlining this process, Quarkus and Micrometer work hand-in-hand to deliver a powerful solution for monitoring microservices with minimal setup.

// gradle dependency for the Quarkus Micrometer extension
implementation 'io.quarkus:quarkus-micrometer:1.11.0.Final'
// gradle dependency for an in-memory registry designed to operate on a pull model
implementation 'io.micrometer:micrometer-registry-prometheus:1.6.3'

What are the two major KPIs of our metrics processing pipeline?

For our metrics processing pipeline, our two major KPIs (Key Performance Indicators) are the number of processed messages and the latency of the whole pipeline across multiple microservices.

We are interested in the number of processed messages over time in order to detect anomalies in the expected workload of the application. Our workload is variable across time but normally follows predictable patterns. This allows us to detect greater than expected load, react accordingly, and proactively detect potential data collection issues.

In addition to the data volume, we are interested in the pipeline latency. This metric is measured for all messages from the first ingestion time to being fully processed. This metric allows us to monitor the health of the pipeline as a whole in conjunction with microservice-specific metrics. It includes the time spent in transit in Kafka clusters between our different microservices. Because we monitor the total processing duration for each message, we can report and alert on average processing time and different percentile values like p50, p95, and p999. This can help detect when one or multiple nodes in a microservice along the pipeline are unhealthy. The average processing duration across all messages might not change much, but the high percentile (p99, p999) will increase, indicating a localized issue.

In addition to our KPIs, Micrometer exposes JVM metrics that can be used for normal application monitoring, such as memory usage, CPU usage, garbage collection, and more.

Using Micrometer annotations

Two dependencies are required to use Micrometer within Quarkus: the Quarkus Micrometer dependency and Micrometer Registry Prometheus. Quarkus Micrometer provides the interfaces and classes needed to instrument codes, and Micrometer Registry Prometheus is an in-memory registry that exposes metrics easily with rest endpoints. Those two dependencies are combined into one extension, starting with Quarkus 1.11.0.Final.

Micrometer annotations in Quarkus produce a simple method to track metric names across different methods. Two key annotations are:

This, however, is limited to methods in a single microservice.

@Timed(
   value = "processMessage",
   description = "How long it takes to process a message"
)
public void processMessage(String message) {
   // Process the message
}

It is also possible to programmatically create and provide values for Timer metrics. This is helpful when you want to instrument a duration, but want to provide individual measurements. We are using this method to track the KPIs for our microservice pipeline. We attach the ingestion timestamp as a Kafka header to each message and can track the time spent throughout the pipeline.

@ApplicationScoped
public class Processor {

   private MeterRegistry registry;
   private Timer timer;

   // Quarkus injects the MeterRegistry
   public Processor(MeterRegistry registry) {
       this.registry = registry;
       timer = Timer.builder("pipelineLatency")
           .description("The latency of the whole pipeline.")
           .publishPercentiles(0.5, 0.75, 0.95, 0.98, 0.99, 0.999)
           .percentilePrecision(3)
           .distributionStatisticExpiry(Duration.ofMinutes(5))
           .register(registry);
   }

   public void processMessage(ConsumerRecord<String, String> message) {
       /*
           Do message processing
        */
       // Retrieve the kafka header
       Optional.ofNullable(message.headers().lastHeader("pipelineIngestionTimestamp"))
           // Get the value of the header
           .map(Header::value)
           // Read the bytes as String
           .map(v -> new String(v, StandardCharsets.UTF_8))
           // Parse as long epoch in millisecond
           .map(v -> {
               try {
                   return Long.parseLong(v);
               } catch (NumberFormatException e) {
                   // The header can't be parsed as a Long
                   return null;
               }
           })
           // Calculate the duration between the start and now
           // If there is a discrepancy in the clocks the calculated
           // duration might be less than 0. Those will be dropped by MicroMeter
           .map(t -> System.currentTimeMillis() - t)
           .ifPresent(d -> timer.record(d, TimeUnit.MILLISECONDS));
   }
}

The timer metric with aggregation can then be retrieved via the REST endpoint at https://quarkusHostname/metrics.

# HELP pipelineLatency_seconds The latency of the whole pipeline.
# TYPE pipelineLatency_seconds summary
pipelineLatency_seconds{quantile="0.5",} 0.271055872
pipelineLatency_seconds{quantile="0.75",} 0.386137088
pipelineLatency_seconds{quantile="0.95",} 0.483130368
pipelineLatency_seconds{quantile="0.98",} 0.48915968
pipelineLatency_seconds{quantile="0.99",} 0.494140416
pipelineLatency_seconds{quantile="0.999",} 0.498072576
pipelineLatency_seconds_count 168.0
pipelineLatency_seconds_sum 42.581
# HELP pipelineLatency_seconds_max The latency of the whole pipeline.
# TYPE pipelineLatency_seconds_max gauge
pipelineLatency_seconds_max 0.498

We then ingest those metrics in LogicMonitor as DataPoints using collectors.

Step-by-step setup for Quarkus Micrometer

To integrate Micrometer with Quarkus for seamless microservice monitoring, follow these steps:

1. Add Dependencies: Add the required Micrometer and Quarkus dependencies to enable metrics collection and reporting for your microservices.

gradle
Copy code
implementation 'io.quarkus:quarkus-micrometer:1.11.0.Final'
implementation 'io.micrometer:micrometer-registry-prometheus:1.6.3'

2. Enable REST endpoint: Configure Micrometer to expose metrics via a REST endpoint, such as /metrics.

3. Use annotations for metrics: Apply Micrometer annotations like @Timed and @Counted to the methods where metrics need to be tracked.

4. Set up a registry: Use Prometheus as a registry to pull metrics from Quarkus via Micrometer. Here’s an example of how to set up a timer:

java
Copy code
Timer timer = Timer.builder("pipelineLatency")
    .description("Latency of the pipeline")
    .publishPercentiles(0.5, 0.75, 0.95, 0.98, 0.99, 0.999)
    .register(registry);

5. Monitor via the endpoint: After setup, retrieve and monitor metrics through the designated REST endpoint:

url
Copy code
https://quarkusHostname/metrics

Practical use cases for using Micrometer in Quarkus

Quarkus and Micrometer offer a strong foundation for monitoring microservices, providing valuable insights for optimizing their performance. Here are some practical applications:

LogicMonitor microservice technology stack  

LogicMonitor’s Metric Pipeline, where we built out multiple microservices with Quarkus in our environment, is deployed on the following technology stack:

Kubernetes node shown in LogicMonitor.

How do we correlate configuration changes to metrics?

Once those metrics are ingested in LogicMonitor, they can be displayed as graphs or integrated into dashboards. They can also be used for alerting and anomaly detections, and in conjunction with ops notes, they can be visualized in relation to infrastructure or configuration changes, as well as other significant events.

Below is an example of an increase in processing duration correlated to deploying a new version. Deploying a new version automatically triggers an ops note that can then be displayed on graphs and dashboards. In this example, this functionality facilitates the correlation between latency increase and service deployment.

An increase in processing duration correlated to the deployment of a new version. The deployment of a new version automatically triggers an ops-note that can then be displayed on graphs and dashboards. In this example, this functionality facilitates the correlation between latency increase and service deployment.

Tips for efficient metrics collection and optimizing performance

To get the most out of Quarkus and Micrometer, follow these best practices for efficient metrics collection:

How to Track Anomalies

All of our microservices are monitored with LogicMonitor. Here’s an example of Anomaly Detection for the pipeline latencies 95 percentile. LogicMonitor dynamically figures out the normal operating values and creates a band of expected values. It’s then possible to define alerts when values fall outside the generated band.

An example of Anomaly Detection for the pipeline latencies 95 percentile in LogicMonitor.

As seen above, the integration of MicroMeter with Quarkus allows in conjunction with LogicMonitor a straightforward, easy, and quick way to add visibility into our microservices. This ensures that our processing pipeline provides the most value to our clients while minimizing the monitoring effort for our engineers, reducing cost, and increasing productivity.

Quarkus With Micrometer: Unlock the Power of Real-Time Insights

Integrating Micrometer with Quarkus empowers real-time visibility into the performance of your microservices with minimal effort. Whether you’re monitoring latency, tracking custom KPIs, or optimizing resource usage, this streamlined approach simplifies metrics collection and enhances operational efficiency.

Leverage the combined strengths of Quarkus and Micrometer to proactively address performance issues, improve scalability, and ensure your services are running at peak efficiency.

FAQs

How does Micrometer work with Quarkus?

Micrometer integrates seamlessly with Quarkus by providing a vendor-neutral interface for collecting and exposing metrics. Quarkus offers an extension that simplifies the integration, allowing users to track JVM and custom metrics via annotations like @Timed and @Counted and expose them through a REST endpoint.

What are the benefits of using Micrometer in a microservice architecture?

Using Micrometer in a microservice architecture provides observability, real-time visibility into the performance of individual services, helping detect anomalies, track latency, and monitor resource usage. It supports integration with popular monitoring systems like Prometheus, enabling efficient metrics collection and analysis across microservices, improving scalability and reliability.

How do you set up Micrometer metrics in Quarkus?

To set up Micrometer metrics in Quarkus, add the necessary dependencies (quarkus-micrometer and a registry like micrometer-registry-prometheus). Enable metrics exposure via a REST endpoint, apply annotations like @Timed to track specific metrics, and configure a registry (e.g., Prometheus) to pull and monitor the metrics.

What are common issues when integrating Micrometer with Quarkus, and how can they be resolved?

Common issues include misconfigured dependencies, failure to expose the metrics endpoint, and incorrect use of annotations. These can be resolved by ensuring that the proper dependencies are included, that the REST endpoint for metrics is correctly configured, and that annotations like @Timed and @Counted are applied to the correct methods.

How do I monitor a Quarkus microservice with Micrometer?

To monitor a Quarkus microservice with Micrometer, add the Micrometer and Prometheus dependencies, configure Micrometer to expose metrics via a REST endpoint, and use annotations like @Timed to track important performance metrics. You can then pull these metrics into a monitoring system like Prometheus or LogicMonitor for visualization and alerting.

If you’re reading this, you already understand the importance of keeping your Apache web servers running smoothly. Whether it’s ensuring you stay within the limits of configured server workers, tracking how many requests are being handled, or guaranteeing maximum uptime, effective Apache monitoring is the key to maintaining server performance and reliability. Fortunately, setting up Apache monitoring is straightforward and can be done in just a few steps.

This guide will take you through a simple, step-by-step process to monitor your Apache servers effectively on operating systems like Windows, covering everything from enabling necessary modules to configuring alerts and integrating with monitoring tools. By the end, you’ll be able to proactively manage your server health, catch potential issues early, and optimize your system for peak performance.

Step 1: Make sure you are loading the mod_status module.

If you are using a version of Apache that was installed by your OS’s package manager, there are OS specific ways to enable modules.

For Ubuntu/Debian:

/usr/sbin/a2enmod status

For Redhat/Centos: Just uncomment the line:

LoadModule status_module modules/mod_status.so

in /etc/httpd/conf/httpd.conf
For Suse derivatives:
add “status” to the list of modules on the line starting with APACHE_MODULES= in /etc/sysconfig/apache2

Step 2: Configure the Mod_status module

You want the following to be loaded in your apache configuration files.

ExtendedStatus On
<Location /server-status>
 SetHandler server-status
 Order deny,allow
 Deny from all
#Add LogicMonitor agent addresses here
 Allow from logicmonitor.com 192.168.10.10
</Location>

Where you set that configuration also changes depending on your Linux distribution.
/etc/apache2/mods-available/status.conf on Ubuntu/Debian
/etc/httpd/conf/httpd.conf on Redhat/CentOs
/etc/apache2/mod_status.conf on OpenSuse/SLES
Finally, restart apache using your OS startup script ( /etc/init.d/httpd restart or /etc/init.d/apache2 restart). Note that using the OS startup script is often necessary to allow the OS specific script files to assemble the final apache config. Sending apache signals, or using apache2ctl, does not do this.

Step 3. Watch the monitoring happen.

If you are using LogicMonitor’s Apache monitoring, then you’re done.  LogicMonitor will automatically detect the Apache web server, and apply appropriate monitoring and alerting, as well as alerting and graphing on the rest of the system, so you can correlate CPU, interface and disk load to Apache load.

One thing you may want to customize is your dashboards – add a widget that collects all Apache requests/second, from all hosts, or all production hosts, and aggregates them into a single graph.  Using LogicMonitor’s flexible graphs, the graph will automatically include new servers as you add them.

Apache Requests per second

Best practices for Apache server monitoring

Establish baselines

Establishing baselines for Apache performance metrics is crucial for effective network monitoring. Baselines help you understand what normal behavior looks like for your servers. By comparing real-time data against these baselines, you can quickly identify anomalies that may indicate issues such as increased traffic or hardware failures.

Automate alerts

Automating alerts is a key way to reduce manual monitoring overhead and ensure timely responses to potential problems. By configuring automated alerts for critical metrics such as CPU load, memory usage, and error rates, you can receive notifications as soon as thresholds are exceeded. This proactive approach allows you to address issues before they escalate, minimizing downtime and ensuring consistent server performance.

Regularly analyzing trends in your monitoring data helps with capacity planning and optimizing performance. Use historical data to identify patterns, such as increased traffic during certain times or resource usage spikes. This enables you to make informed decisions about scaling infrastructure, optimizing configurations, and planning for future growth. Trend analysis also allows you to fine-tune alert thresholds to reduce false positives and improve the accuracy of your monitoring system.

Tracing and automation

Implementing tracing and automation workflows enhances Apache server monitoring by automating alerts and analyzing trends. Tracing tracks request paths, offering insights into response times, errors, and dependencies to identify bottlenecks and optimize performance.

Automation workflows enable you to streamline repetitive tasks such as log analysis, performance testing, and restarts. By automating these processes, you can focus on more critical tasks while ensuring consistency and efficiency in your monitoring efforts. Version pinning specifies exact software versions, reducing compatibility issues and simplifying troubleshooting.

Ready to simplify your Apache monitoring?

Monitoring your Apache http servers is essential for maintaining optimal performance, ensuring availability, and preventing issues before they escalate. By understanding key metrics, integrating powerful monitoring tools, and setting up proactive alerts, you can stay ahead of server problems and ensure your infrastructure remains healthy and efficient.

If you’re looking to simplify your Apache monitoring, consider using LogicMonitor. LogicMonitor automates the setup, detection, and visualization of your Apache environment, making it easier to identify issues, set up alerts, and aggregate critical metrics. With LogicMonitor, you can save time, reduce manual effort, and ensure comprehensive coverage of your Apache infrastructure.

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Name resolution is a critical component of network management, allowing systems to translate human-friendly domain names into IP addresses. However, discrepancies between tools like ping and DNS can lead to confusion and potential monitoring inaccuracies.

This article explores why these discrepancies occur and provides guidance on troubleshooting and resolving these issues.

How ping and DNS differ in name resolution: Common causes of discrepancies

Most people know their hosts via DNS names (e.g. server1.lax.company.com) rather than IP addresses (192.168.3.45), and so enter them into their monitoring systems as DNS names. Thus, there is a strong requirement that name resolution works as expected in order to make sure that the monitoring system is, in fact, monitoring what the user expects it to be.

Sometimes, we get support requests about how the LogicMonitor collector is resolving a DNS name to an IP address incorrectly, but the DNS is all set up as it should be, so something is wrong with the collector. However, the issue is simply in the interactions of how hosts resolve names, which is not always the same as how DNS resolves names.

The confusion lies in the fact that the tools people often use to validate their name resolution setup – host and nslookup – only use the name resolution system. They talk to the name servers listed in /etc/resolv.conf (or passed to them by their Active Directory configuration), and ask the name servers about what a particular host resolves as.

However, Windows and Linux do not just use the DNS system. They have other sources of resolving names – the /etc/hosts file on linux,WindowsSystem32driversetchosts on Windows, NIS, NetBIOS name resolution, caching systems like nscd – none of which are consulted by host or nslookup, but any of which may return conflicting information that the operating system may use.

As a simple example, you can see below that there is a local entry defining the address of foo.com to be 10.1.1.1:

 [[email protected]:~]$ cat /etc/hosts
 127.0.0.1 logicmonitor.com logicmonitor.com.localdomain logicmonitor.com4 logicmonitor.com4.localdomain4
 ::1 logicmonitor.com logicmonitor.com.localdomain logicmonitor.com6 logicmonitor.com6.localdomain6
 10.1.1.1 foo.com

While the ping program uses the locally configured address:

[[email protected]:~]$ ping foo.com
PING foo.com (10.1.1.1) 56(84) bytes of data.
^C
--- foo.com ping statistics ---
2 packets transmitted, 0 received, 100% packet loss, time 1725ms

The host and nslookup programs do not:

[[email protected]:~]$ host foo.com
foo.com has address 23.21.224.150
foo.com has address 23.21.179.138
foo.com mail is handled by 1000 0.0.0.0.
[[email protected]:~]$ nslookup foo.com
Server: 216.52.126.1
Address: 216.52.126.1#53
Non-authoritative answer:
Name: foo.com
Address: 23.21.224.150

Comparison of Ping vs DNS Name Resolution

Ping and DNS resolve names differently due to the varied sources they consult. Below is a comparison of these tools:

AspectPingDNS (nslookup, host)
Source of ResolutionLocal hosts files, NetBIOS, NIS, caching systemsOnly DNS servers listed in /etc/resolv.conf
Impact of CachingMay use stale cached data (e.g., nscd)Typically reflects current DNS server data
Local OverridesUses /etc/hosts and similar local sourcesIgnores local entries, only queries DNS servers

Troubleshooting steps for resolving name resolution discrepancies

If you encounter discrepancies between how the ping command resolves a DNS name and the expected DNS results, follow these steps to pinpoint and resolve the issue:

  1. Check Local Hosts Files
    First, inspect the local hosts file on your system (/etc/hosts on Linux or C:\Windows\System32\drivers\etc\hosts on Windows). Entries in these files can override DNS settings, causing ping to resolve a name differently than tools like nslookup or host. Look for any entries that may be directing traffic to an unexpected IP address.
  2. Flush DNS and Name Service Caches
    Caching can often be the culprit behind outdated or incorrect name resolution. Use commands such as ipconfig /flushdns on Windows or sudo systemd-resolve –flush-caches on Linux to clear DNS caches. Additionally, if you’re using a name service cache daemon like nscd, restart it with sudo systemctl restart nscd to ensure it’s not serving stale data.
  3. Review Name Resolution Order
    On Linux systems, the order of name resolution is determined by the nsswitch.conf file. This file specifies which services to query (e.g., DNS, files, NIS) and in what order. Misconfigurations here can lead to unexpected results. Ensure the file is set up correctly and reflects the desired order of resolution.
  4. Compare Results with Nslookup or Host
    Use nslookup or host to query your DNS server names directly. This will show you the IP address that DNS servers are returning for a given hostname. Compare these results with what ping is showing. If nslookup provides the correct IP address while ping does not, you’ve confirmed that the issue lies outside of DNS, likely due to local overrides or caching.
  5. Examine Network Configuration and Overrides
    Network settings, including VPNs, proxy configurations, or split DNS setups, can affect name resolution. Check your network settings and look for any rules or overrides that could be directing your queries differently based on your network context.

By systematically reviewing these factors, you can identify the root cause of name resolution discrepancies and ensure your monitoring and diagnostic tools are functioning as expected. Always keep in mind the source each tool is using, and adjust configurations as needed to maintain consistent and reliable name resolution across your systems.

Take control of your network monitoring with LogicMonitor

So the moral of the story? Know where the tool you are using is getting its information from. If it is nslookup or host, it is only querying the Domain Name system. The operating system (ping, telnet, etc) may well be using other sources of information.

Don’t let name resolution discrepancies compromise your network performance. LogicMonitor provides comprehensive insights into your network’s health, helping you pinpoint and resolve issues swiftly. With advanced monitoring tools that factor in all name resolution sources, LogicMonitor ensures that your monitoring data reflects the true state of your network.

AWS (Amazon Web Services) releases new products at an astounding rate, making it hard for users to keep up with best practices and use cases for those services. For IT teams, the risk is that they will miss out on the release of AWS services that can improve business operations, save them money, and optimize IT performance.

Let’s revisit a particularly underutilized service. Amazon’s T2 instance types are not new, but they can seem complicated to someone who is not intimately familiar. In the words of Amazon, “T2 instances are for workloads that don’t use the full CPU often or consistently, but occasionally need to burst to higher CPU performance.” This definition seems vague, though.  

What happens when the instance uses the CPU more than “often”? How is that manifested in actual performance? How do we reconcile wildly varying CloudWatch and OS statistics, such as those below?

screen-shot-2016-12-16-at-9-33-56-am screen-shot-2016-12-16-at-9-34-04-am

Let’s dive in to explore these questions.

How CPU credits work on T2 instances

Amazon explains that “T2 instances’ baseline performance and ability to burst are governed by CPU credits. Each T2 instance receives CPU credits continuously, the rate of which depends on the instance size. T2 instances accumulate CPU credits when they are idle and use them when they are active. A CPU credit provides the performance of a full CPU core for one minute.” So the instance is constantly “fed” CPU credits and consumes them when the CPU is active. If the consumption rate exceeds the feeding rate, the CPUCreditBalance (a metric visible in CloudWatch) will increase. Otherwise, it will decrease (or stay the same). This dynamic defines T2 instances as part of AWS’s burstable instance family.

Let’s make this less abstract: Looking at a T2.medium, Amazon says it has a baseline allocation of 40% of one vCPU and earns credits at the rate of 24 per hour (each credit representing one vCPU running at 100% for one minute; so earning 24 credits per hour allows you to run the instance at the baseline of 40% of one vCPU). This allocation is spread across the two cores of the T2.medium instance. 

An important thing to note is that the CPU credits are used to maintain your base performance level—the base performance level is not given in addition to the credits you earn. So effectively, this means that you can maintain a CPU load of 20% on a dual-core T2.medium (as the two cores at 20% combine to the 40% baseline allocation). 

In real life, you’ll get slightly more than 20%, as sometimes you will be completely out of credits, but Amazon will still allow you to do the 40% baseline work. Other times, you will briefly have a credit balance, and you’ll be able to get more than the baseline for a short period.

For example, looking at a T2.medium instance running a high workload, so it has used all its credits, you can see from the LogicMonitor CloudWatch monitoring graphs that Amazon thinks this instance is constantly running at 21.7%:


screen-shot-2016-12-12-at-5-01-56-pm

This instance consumes 0.43 CPU credits per minute (with a constant balance of zero, so it consumes all the credits as fast as they are allocated). So, in fact, this instance gets 25.8 usage credits per hour (.43 * 60 minutes), not the theoretical 24.

AWS RDS instances also use CPU credits, but the calculation is a bit different and depends on instance size and class (general purpose vs memory optimized). The T2 burst model allows T2 instances to be priced lower than other instance types, but only if you manage them effectively.


screen-shot-2016-12-12-at-5-04-22-pm

Impact of CPU credit balance on performance

But how does this affect the instance’s performance? Amazon thinks the instance is running at CPU 21% utilization (as reported by CloudWatch). What does the operating system think?

Looking at operating system performance statistics for the same instance, we see a very different picture:

screen-shot-2016-12-12-at-5-53-33-pm


Despite what CloudWatch shows, utilization is not constant but jumps around with peaks and sustained loads. How can we reconcile the two? According to CloudWatch, the system uses 21% of the available node resources when it is running at 12% per the operating system and 21% when it is running at 80% per the operating system. Huh?

It helps to think of things a bit differently. Think of the 21% as “the total work that can be done within the current constraint imposed by the CPU credits.” Let’s call this 21 work units per second. The operating system is unaware of this constraint, so asking the OS to do the total work that can be done with 21 work units will get that done in a second and then be idle. It will think it could have done more work if it had more work—so it will report it was busy for 1 second, idle for the next 59 seconds—or 1.6% busy. 

However, that doesn’t mean the computer could have done 98% more work in the first second. Ask the computer to do 42 work units, and it will take 2 seconds to churn it out, so the latency to complete the task will double, even though it looks like the OS has lots of idle CPU power.

We can see this in simple benchmarks: On two identical T2.medium instances with the same workload, you can see very different times to complete the same work. One with plenty of CPU credits will complete a sysbench test much quicker:

sysbench --test=cpu --cpu-max-prime=2000 run

sysbench 0.4.12:
  multi-threaded system evaluation benchmark


Number of threads: 1


Maximum prime number checked in CPU test: 2000

Test execution summary:

    total time:                          1.3148s

    total number of events:              10000

While an identical instance, but with zero CPU credits, will take much longer to do the same work:

Test execution summary:

    total time:                          9.5517s

    total number of events:              10000

Both systems reported, from the OS level, 50% CPU load (single core of dual core system running at 100%). But even though they are identical ‘hardware’, they took vastly different amounts of time to do the same work.

This means a CPU can be “busy” but not work when it’s out of credits and finished its base allocation. It appears very similar to the “CPU Ready” counter in VMware environments, indicating that the guest OS has work to do but cannot schedule a CPU. After running out of CPU credits, the “idle” and “busy” CPU performance metrics indicate the ability to put more work on the processor queue, not the ability to do more work. And, of course, when you have more things in the queue, you have more latency.

Monitoring and managing CPU credit usage

So, clearly, you need to pay attention to the CPU credits. Easy enough to do if you are using LogicMonitor—the T2 Instance Credits DataSource does this automatically for you. (This may already be in your account, or it can be imported from the core repository.) This DataSource plots the CPU credit balance and the rate at which they are being consumed, so you can easily see your credit behavior in the context of your OS and CloudWatch statistics:

screen-shot-2016-12-14-at-10-57-31-am
screen-shot-2016-12-14-at-10-57-50-am

screen-shot-2016-12-14-at-10-58-10-am
This DataSource also alerts you when you run out of CPU credits on your instance, so you’ll know if your sudden spike in apparent CPU usage is due to being throttled by Amazon or by an actual increase in workload.
screen-shot-2016-12-16-at-9-41-42-am

What are burstable instances?

Burstable instances are a unique class of Amazon EC2 instances designed for workloads with variable CPU usage patterns. They come with a baseline level of performance and the ability to burst above it when your workload requires more CPU resources.

Each burstable AWS EC2 instance has a few baseline characteristics:

This capability makes burstable instances ideal for applications with a sometimes unpredictable traffic load. Some common use cases you see them used for include:

T2s aren’t the only product that allows for burstable instances, either. They are also included in the following product families:

What are T3 instances?

T3 instances are Amazon’s next generation in the AWS T family of burstable instances. T3 offers improved performance and a better cost—making it a great choice for your business if you plan to start with AWS or upgrade your current instance.

T3 offers many benefits over T2:

Overall, Amazon’s T3 lineup offers a substantial advantage over T2 in performance and cost. Look at your options to determine if it’s right for your organization.

Best practices for optimizing T2 instance performance

So, what do you do if you get an alert that you’ve run out of CPU credits? Does it matter? Well, like most things, it depends. If your instance is used for a latency-sensitive application, then this absolutely matters, as it means your CPU capacity is reduced, tasks will be queued, and having an idle CPU no longer means you have unused capacity. For some applications, this is OK. For some, it will ruin the end-user experience. So, having a monitoring system that can monitor all aspects of the system—the CloudWatch data, the OS-level data, and the application performance—is key.

Another note: T2 instances are the cheapest instance type per GB of memory. If you need memory but can handle the baseline CPU performance, running a T2 instance may be a reasonable choice, even though you consume all the CPU credits all the time.

Hopefully, that was a useful breakdown of the real-world effect of exhausting your CPU credits.

At the heart of LogicMonitor’s monitoring solution is the LogicMonitor Collector, a crucial application that gathers device data and sends it to the LogicMonitor platform. This real-time monitoring feature tracks the health and performance of Collectors and ensures continuous data collection by sending alerts about potential issues before they escalate. When issues arise, understanding the Collector Status is key to quickly resolving them. 

This guide walks through steps for troubleshooting issues related to the Collector Status, ensuring that the monitoring setup remains reliable and effective.

What is Collector Status?

Collector Status provides real-time insights into the health and performance of LogicMonitor Collectors. It tracks essential metrics such as CPU load, memory usage, and network connectivity, sending notifications to users about potential issues before they escalate into major problems. Regular monitoring of the Collector Status prevents downtime, optimizes performance, ensures continuous data collection, and gives the ability to personalize solutions.

Step 1: Check the Collector and Watchdog services

The first step in troubleshooting is to validate that the LogicMonitor Collector and Watchdog services are running properly on the host machine. These services are essential for maintaining communication between devices and the LogicMonitor platform. If either service is down, the status of the Collector will reflect this, and gaps in monitoring data may become apparent.

Learn more about troubleshooting and managing Collector services.

Step 2: Verify credentials and permissions

Incorrect credentials or insufficient permissions can cause the Collector to fail to communicate with your monitored devices, which will be reflected in the Collector Status. This is a common issue, particularly in Windows environments.

Step 3: Check the Collector connection to LogicMonitor servers

A common reason for a degraded Collector Status is connectivity issues. The LogicMonitor Collector needs to connect to LogicMonitor’s cloud servers over port 443 using HTTPS/TLS. If this connection is interrupted, the Collector cannot send data, and monitoring will be disrupted.

Step 4: Review antivirus software settings

Antivirus software can sometimes interfere with the Collector’s operation by blocking necessary files or processes. This can lead to a poor Collector Status as the Collector may not be able to perform its functions correctly.

Step 5: Monitor Collector health with Collector Status

The Collector Status in LogicMonitor is the primary tool for monitoring the health and performance of Collectors. Regularly reviewing the Collector Status can help to identify potential issues, such as high CPU load, memory overuse, or connectivity problems, before they lead to downtime.

Explore LogicMonitor’s guide to best practices for optimizing Collector performance.

The Collector Status Option when managing a collector can help troubleshoot collector issues.

Collector Status is a great place to check on Collector health. It can indicate potentially problematic load issues and LogicModules with abnormally high numbers of failed polls. 

The top of the Collector Status gives a quick overview of the status of the varying metrics that make it up. Warning and Error status items should be investigated further.
The various metrics that make up Collector Status can indicate potential load related problems before they become a problem. These change color to indicate potential problems and contain helpful messages.

Collector Status is not intended to provide a complete view of Collector performance but is an excellent tool for quickly identifying the source of issues. It offers several features that help IT teams quickly pinpoint problems and get an overview of a Collector’s overall health:

The Collector also tracks restarts and errors reported by Watchdog, which is very useful when looking for patterns that indicate problems.

Collector Events for a healthy Collector showing it’s daily restart and credential rotation.

Step 6: Set up resilient monitoring

To further protect the monitoring setup, consider implementing resilient monitoring strategies. This includes setting up a backup Collector or using an Auto-Balanced Collector Group to distribute the monitoring load across multiple Collectors. This helps maintain a healthy Collector Status and ensures that monitoring continues without interruption, even if one Collector goes down.

LogicMonitor’s article, Collector Capacity, offers a broader understanding of how Collectors handle workloads.

Maintain a healthy Collector Status

Understanding and regularly checking the Collector Status ensures that LogicMonitor Collectors are performing optimally and providing continuous and reliable monitoring for IT infrastructures. Implementing the steps outlined in this troubleshooting guide can help resolve issues that arise and guide the setup of a resilient monitoring system that protects against future problems.