Edge computing enables bringing computing close to where data is generated. Edge computing redefines the ITOps in a different way. This calls for much-advanced solution to monitor the entire platform. AI-based observability is the perfect solution for your entire edge computing IT operations. This solution will help to gain end-to-end visibility across your entire edge cloud operations, ensuring low latency and better bandwidth use.
Edge computing observability is an AI driven method that enables to provide a unified observability across edge platform. By leveraging KubeEdge technology along with AIOps, native containerized application orchestration is simplified to a great extent. Also, telemetry and observability can be achieved without any agents.
With the incorporation of AI-based observability, automated collection and analysis of structured and unstructured data is made possible in real-time. This enables to reduce the time IT operators need to spend in data collection and processing them. The AI-based models are capable of handling them effectively.
A major challenge faced is in identifying unknown issues from huge volume of data. With AI-based observability, the system is capable of automatically ingesting data and then proactively identify anomalies such as KPI anomalies and log anomalies. This helps the IT operators to get intelligent alerts and enables them to resolve the issue before persisting to bigger problems.
With the use of AI-based models for edge computing observability, this enabled in reducing the major concerns of the cloud cost optimization process. Most of the actions were automated with the help of AI models. Also, by right-sizing the Kubernetes to meet SLOs, operators can most effectively use available infrastructure resources.