AIOps for Hybrid Observability
LogicMonitor has a layered approach to intelligence, where AI and Machine Learning is baked into every facet of the LM Envision platform. Reduce MTTI/MTTR, improve efficiency, minimize alert fatigue and transform your IT team from reactive to proactive.
Simplify & Scale: LogicMonitor’s Latest Innovations
LogicMonitor’s latest product innovations deliver on our commitment to creating a unified experience, supported by a strong foundation in layered intelligence and hybrid observability.
LM Co-Pilot: Your AI Co-Pilot for the Magical Streamlining of IT and Cloud Operations
LM Co-Pilot is crafted to serve as the key assistant that empowers IT and cloud professionals to navigate their daily whirlwind of identifying issues, prioritizing critical matters, and providing pragmatic solutions. Learn more!
Dexda Solution Brief
Dexda, LogicMonitor’s AI for hybrid observability solution, was built to solve ITOps teams challenges by silencing redundant monitoring alerts and providing universal context to expedite troubleshooting efforts across teams – all while reducing MTTR in a hybrid environment.
AIOps for Monitoring eBook
As the world continues to embrace automation, IT teams can finally focus on growth and innovation. The goal is to pivot from manual, repetitive work to more abstract and strategic problem solving that can’t be automated. Artificial Intelligence (AI) is leading the charge. This eBook illustrates and defines AIOps, key uses, and trends in development.
AIOps Q3 2023 Constellation Shortlist
The Constellation ShortList presents vendors in different categories of the market relevant to early adopters. In addition, products included in this document meet the threshold criteria for this category as determined by Constellation Research.
Unlocking intelligence & extensibility: LogicMonitor’s latest product innovations
Join our webinar with LogicMonitor’s Chief Product Officer, Taggart Matthieson, and LogicMonitor’s Senior Director of Product Marketing, Bill Emmett, for a conversation about how our recent product innovations can help you unlock intelligence and extensibility in your hybrid IT environments.
Dexda: AI for Hybrid Observability
LogicMonitor’s enterprise capabilities, powered by Dexda, ingest events and seamlessly transform them into episodes to reduce alert noise by up to 80%.
Advanced machine learning techniques automatically identify features in the alert data to correlate the disparate alerts into connected insights based on time, resources involved, environment, and other significant features of the enriched alert data.
Using advanced machine learning and natural language processing (NLP) algorithms, Dexda helps ITOps teams effortlessly identify problems, determine the root cause of those problems faster than ever before, and prevent events from exploding into business-critical incidents.
The Dexda Difference
Quick Time to Value
Get started with Dexda immediately. Dexda employs out-of-box ML models with no need for training and includes a seamless integration with LogicMonitor. With multi-tenancy, Dexda is completely scalable and MSP-ready with correlations scoped to each tenant, so you can help your customer quickly identify incidents.
With Dexda’s open and customizable machine-learning models, users can define their own correlation models to target the alert and enriched CMDB data that makes sense for their business. In addition, using Adaptive correlation, Dexda automatically re-clusters alerts when it identifies a more optimal clustering option. This avoids any delay in escalating insights to ServiceNow.
Dexda integrates seamlessly with the ServiceNow Incident module for full bi-directional synchronization of alerts in Dexda and incidents in ServiceNow. Dexda event episodes are enriched with ServiceNow CMDB information so responders have additional context for rapid problem identification and resolution.
Adaptable Alert Clustering
Many teams struggle with too many alerts, especially when the same alerts are repeatedly created. Dexda clusters alerts using AI-driven methods across time, infrastructure and other items to convert hundreds of alerts into a single episode, which can be used to automatically open an incident in ServiceNow and get enriched with ServiceNow CMDB information to accelerate troubleshooting.
The AIOps Early Warning System
LogicMonitor’s Early Warning System will detect the warning signs and symptoms that precede issues, such as patterns or anomalies in alerts or performance data, and warn users accordingly. These early warnings will be able to trigger actions, such as integrations and custom scripts, to prevent issue occurrence. By warning users sooner, this Early Warning System will help enterprises prevent outages, saving them time, money, and avoid negative impact on their brands.
Datapoint Analysis – Metric Correlation for faster RCA
Without Datapoint Analysis, ITOps teams must manually correlate metrics across various resources. Datapoint Analysis automates metric correlation so teams can get to the root cause faster than ever before.
For example, if CPU on a VM is spiking, Datapoint Analysis can show you what other metrics were showing similar behavior immediately before and during the incident. For instance, perhaps memory or network traffic spiked across different VMs. This helps you get to a common root cause faster.
Dynamic Thresholds – Preventive Early Warning System
Increase IT Efficiency and detect issues sooner. Before an issue becomes catastrophic, Dynamic Thresholds can warn you so you can take preventive measures.
Dynamic thresholds use anomaly detection algorithms to detect a resource’s expected range based on past performance and limit alert notifications to those that correspond to values outside of this range (i.e. anomalies). Dynamic thresholds also reduce noise where static thresholds aren’t tuned well, so you can ensure your team is focusing on what’s really important.
Forecasting – Predict trends for capacity planning
Forecasting helps you proactively prevent issues, budget plan and manage resources so you can prevent downtime and keep your business services operating efficiently. Predict the health and performance of your critical infrastructure and determine whether an issue represents a one-time anomaly, requires immediate attention, or will require attention in the near future.
Root Cause Analysis – Suppress dependent alert notifications
With root cause analysis, LogicMonitor uses automatically discovered relationships between monitored resources to identify the root cause for triggered alerts and notify users of the originating issue, while suppressing notifications for dependent resources in alert. When a core or root device goes down affecting connectivity for downstream devices, Root Cause Analysis (RCA) will identify the originating and dependent resources and subsequent alerts and disable notifications for dependent resources.
Turn Alerts into Episodes – Alert Clustering
Many teams struggle with too many alerts, especially when the same alerts are repeatedly created. LogicMonitor’s new Dexda product, clusters alerts using AI-driven methods across time, infrastructure and other items to convert hundreds of alerts into a single episode, which can be used to automatically open an incident in ServiceNow and get enriched with ServiceNow CMDB information to accelerate troubleshooting.
The bottom line: Dexda is a no-brainer addition for existing LogicMonitor observability customers
Andy Thurai, Vice President and Principal Analyst Constellation Research
AiOps and dynamic thresholds help our customers with easy-to-understand trend forecasting and proactive insights into their environment
Steve N., Senior Cloud Systems Engineer Ascend Technologies Group
Using LogicMonitor’s AIOps Early Warning System, you can easily see and understand potential issues in the system and be more proactive in resolving them. This is a great feature that is helpful in many use cases across IT infrastructures.
IDAN LERER, SR. DIRECTOR, US OPERATIONS OPTIMALPLUS
Linux machines notoriously generate lots of CPU performance alerts. These machines are being highly utilized intentionally and well within their limits, but it’s creating noise, with LogicMonitor’s dynamic thresholds, we only get alerted when CPU is truly abnormal.
JASON SMITH, ASSOCIATE DIRECTOR AGIO
AIOps frequently asked questions
- What is AIOps?
AIOps, which stands for Artificial Intelligence for IT Operations, is a method for analyzing and displaying data for IT teams based on machine learning algorithms. The AI used in AIOps is often based on historical patterns coupled with current learned data trends.
- Is LM’s AIOps really using AI?
Yes. LogicMonitor’s AIOps goes beyond simple machine learning and pattern detection to learn and report based on individual relationships within each company’s tech stack.
- Does LogicMonitor use customer data to train their models?
For Dexda, the AI engine is pre-trained and the models do not combine data from other customers. In features like dynamic thresholds, we use your historical data but it’s only your data – we do not combine it with other customers’ private data.
- What is root cause analysis?
Root cause analysis is the process of finding the core of an issue that caused a chain reaction effect ending in problems.
- What is anomaly detection?
Anomaly detection is the identification and notification of outliers within gathered datapoints. An anomalous datapoint is something that significantly deviates from a normal data range without reason.
- What are dynamic thresholds?
Dynamic thresholds are dataranges that show an acceptable changing range of datapoints based on similar historical factors.
- What is machine learning?
Machine learning is the use of algorithms that improve automatically through historical analysis and experience.
- What is Dexda?
Dexda is LogicMonitor’s enterprise AIOPs event management offering. Dexda ingests events from the LogicMonitor platform and seamlessly transforms them into episodes. Advanced machine learning techniques automatically identify features in the alert data to correlate the disparate alerts into connected insights based on time, resources involved, environment, and other significant features of the enriched alert data.
- What is event clustering?
Event clustering automatically groups event alerts in a correlation into their most succinct form, vastly reducing the time it takes for support teams to reason about the mass of alerts. Effective event clustering can reduce alerts by over 97%.