Enterprises are increasingly shifting to microservices architectures that are owned by teams instead of large scale applications. AIOps enables these teams to address the unique challenges that come with microservices-based applications by providing actionable intelligence and context-specific alerts.
While this creates greater amount of observability points, it also adds to the chaos of information overload for IT Centres of Excellence teams. With so many moving components and information passed along messaging protocols, the amount of noisy alerts has kept growing too high for humans alone to handle. AIOps addresses these challenges by monitoring the telemetry data in the context of their topology using cloud-native OpenTelemetry standards. AIOps powered with dependency mapping and multi-variate KPI analysis filters down alerts and pain points to find real issues. Consequently, CoEs have actionable intelligence that drives better decision-making and ITOps teams are faster in remediation.
While it is easier to scale up microservices, the challenges of observing large scale deployments becomes very complex as horizontal services frequently influence each other's behaviour due to load balancing or can be deployed in multi cloud environments. Fortunately, AIOps can scale up monitoring just as easily with more training data for it to observe and learn from.
Microservices rely on a chain of independent services for handling user requests. Minor issues in one point can traverse along and create a false alert in another service easily. This can waste hours of ITOps team time as they track the problems down to the real issues and cross-check each service documentation. AIOps speeds this up by automating this root cause analysis and greatly boosting ITOps productivity.
Microservices are by nature distributed across different environments. These make them vulnerable if authentications and access to services are not handled correctly. Testing these issues becomes just as challenging over multiple environments. AIOps manages this complexity with smart detection and analysis of irregular behaviour. By finding such anomalies, AIOps enables stronger security of deployed microservices software.
