Cloud-Native Observability - An AI-driven approach.

Mar 4, 2021. By Anil Abraham Kuriakose

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Cloud-Native Observability - An AI-driven approach

Cloud-native architecture disrupts the IT systems, enabling the IT team to lead the organization as a business partner. Even though it provides many scalability and performance advantages, it reduces the manageability of the entire system. The cloud-native systems create a lot of accessibility and opacity issues because the network layer is completely hidden. So the cloud-native systems should go beyond traditional monitoring to real-time monitoring.


IT Monitoring is actually collecting and displaying system data. But with monitoring, it is very difficult to derive a complete picture of the system's internal state. This is where observability plays a more significant role. The term "observability" in control theory states that the system is observable if the system's internal states and, accordingly, its behavior can be determined by only looking at its inputs and outputs. Observability extends IT monitoring principles by pulling together data from logs, metrics, traces, and events to empower operators to identify root causes of issues and resolve them quickly. For current IT systems, observability requires all kinds of instrumentation to capture all its inputs and outputs. Cloud-native systems are much complex and distributed in nature. Hence it requires new forms of deeper, context-sensitive telemetry mechanism to observe and understand what is happening inside it. The cloud-native observability systems should look beyond data at rest to streaming, continuous, and real-time data.

Cloud-Native Observability

The cloud-native technology itself can provide a solution to this problem. We can use a containerized microservice agent for collecting real-time data from cloud-native sources. The agent can act as an event-driven in-node process to collect detailed observability from the kernel and network namespaces, using extended Berkeley Packet Filter (eBPF) to produce detailed telemetry. Instead of relying on static counters and gauges exposed by the operating system, eBPF enables the collection of in-kernel aggregation of dynamic metrics. The eBPF has secure, resource-efficient access to functions and services at the operating system (kernel) level and directly enables the telemetry up to the application level. But the challenge here is analyzing a massive volume of data in real-time. Along with this data explosion, the more recent technologies are dynamic or even ephemeral, and sampling becomes increasingly impractical and ineffective. Now the right solution is applying Artificial Intelligence to get the maximum out of the telemetered data.

AI in Cloud Native Observability

AI provides many analytical and pattern detection capabilities that help the IT operations team automate the problem detection and root cause analysis processes. Using the AIOps approach, IT teams can uncover anomalies in the operations data. Also, it can provide more personalized and contextual insights driven by advanced AI techniques. These capabilities help IT organizations and teams to make better and faster decisions. AIOps tools serve the observability and telemetry streaming data in real-time through the AI pipeline's cascaded models. The real-time analytical capability of the AIOps models overcomes the dynamic challenges created by the Cloud Native telemetry data.

Benefits of AI-driven Cloud-Native Observability

1) The AI-driven cloud-native systems allow a deep monitoring and event correlation for the cloud-native observability problems. The IT team needs to work only on the real issues instead of fire fighting in the noise issues and false positives. 2) Able to use newer cloud-native technologies along with your existing system and get a combined advanced statistics and KPIs. 3) It saves operations through automated remediation and reduces the meantime to repair (MTTR). Hence IT team can improve the IT service availability and manages their SLAs. 4) Enable the DevOps team to get better feedback about the application performance and provide a capability to analyze the development environment.

To learn more about Algomox AIOps Observability, please visit our Cognitive Observer Manager Page

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