In 1927, the world was introduced to the origins of Artificial Intelligence (AI) in the form of a robot in the movie Metropolis. Throughout nearly a century since then, movies have continued to iterate on the complexities of AI, as both a fun take on it and serious commentary on the potential concerns and consequences. This is all well and good, but as AI has continued to evolve, we find ourselves asking, “how can we actually use this to make our lives easier?”
Simply, using a very specific set of guidelines, IT infrastructure is impacted by AI as it yearns to take away some of the menial tasks, allowing IT professionals to work on more interesting and complex tasks. These AI helpers come in a variety of forms. Today I want to discuss the simple, single-task AI’s that exist in cloud infrastructure as well as more complex AIOps that make monitoring and managing your cloud infrastructure easier.
All cloud services have some variety of Auto Scaling that allows the infrastructure to spin itself up and down to handle the load and expected load. The most simple form of this is scheduled scaling, which is set by a person. These scheduled scalings are often superseded by allowing the system to determine when the load is too much and allowing it to scale.
In Amazon Web Service (AWS), their service called Auto Scaling allows users to set parameters for scaling services to handle demand and it can be used across multiple services. Microsoft Azure continues the naming sequence with its service Autoscale, which allows you to scale by any metric and is designed to “save money by not wasting servers”. Not to be outdone, Google Cloud Platform (GCP) also allows scaling of services similar to AWS and Azure.
These auto-scaling services offered by the cloud providers exist to leverage smart metric monitoring to allow maximum throughput for minimal money. Simple versions of this have been seen in IT infrastructure for years in the form of load balancers for network traffic. Load balancers have become more complex over time and each of the clouds implements network load balancing services. It’s no wonder that cinema has taken hold of the idea of AI when we can see how helpful a simple form of AI can be for helping maintain IT infrastructure.
As these cloud-based services have continued to implement AI, a new task has emerged. How do we know if our AI-based scaling is operating the way it should be? A monitoring alert to say that a system is getting close to its maximum load may be entirely in vain if that system has some version of autoscaling. This maximum could just be the trigger that spins up new instances to handle this new load.
By leveraging AI, you can have infrastructure monitoring that learns when an issue is real, verse when it is just triggering new cloud instances. This smart version of monitoring can also be leveraged to know when too many new instances are being spun up in a certain timeframe, allowing you to investigate underlying conditions. As AIOps become larger scale, more widely used, and better tuned, we can continue to grow in our understanding of cloud infrastructure and monitoring. Trusting AI to help determine when there may be a problem and alert us only when there is a real issue frees up time to work on higher-order tasks and insight.
As long as we do not continue down the path of cinema AI in the likes of Her or Ex Machina, we should be able to trust and use AI to help our IT Operations teams be more efficient, understand the complexities of their infrastructure, and remain safely in control of the important parts of our IT infrastructure.
To learn more about how LogicMonitor monitor is using AIOps to help monitor and understand your infrastructure or to see it in action, sign up for a free trial.
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Brian is enjoying the transition from software engineer to product manager. He loves leveraging his past experience and learning more about the broad scope of the products he contributes to. Brian is an animal enthusiast with many resident pets and often at least a couple of foster animals as well.
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