KubeInsight
InsightFinder’s KubeInsight offers comprehensive integration capabilities, enabling the collection of metrics, logs, and event data directly from pods and containers within a Kubernetes cluster.
KubeInsight’s reach extends to AWS EKS, Google Cloud GKE and on-prem platforms analyzing all machine data making it a versatile solution for multi-cloud strategies.
Data collection is done through a kubernetes collector to capture data from the cluster.
Metrics
- CPU Usage
- DiskSpace Usage
- Memory
- Requests
- Limits
- Request Usage
- Limits Usage
- Network I/O
- Disk I/O
Event
- Container Status Events
- Pod Crash Events
- Scaling Events
Log
- Container Logs
Trace
- Trace data from microservices
- Dependency map of microservice traffic
InsightFinder collects a wide range of data points to help businesses make informed decisions. The platform collects critical performance metrics like CPU usage, memory usage, disk space usage, requests, and limits. It also offers visibility into network I/O, disk I/O, and container status events, crash events, and scaling events.
Prediction-Driven Autoscaling
InsightFinder also supports the autoscaling of memory and CPU resources based on our predictions. KubeInsight’s ability to auto-scale helps businesses optimize performance during seasons of heavy workloads. In addition to performance, it delivers an average of 60% reduction in infrastructure costs by downscaling unneeded resources during times of light workloads.
Scale down based on prediction
InsightFinder predicts future memory requirements, enabling proactive reduction of memory allocations. When lower usage is forecasted, the autoscaler decreases memory requests, aligning resource allocation with predicted needs.
Scale Up based on prediction
Conversely, InsightFinder can preemptively increase resource limits and requests. By forecasting higher resource demands, it automatically scales up memory allocations, preventing issues related to resource scarcity.
Scale Up based on Predicted and Detected Incidents
InsightFinder actively predicts and detects incidents in realtime. This functionality permits tailored scaling actions in response to specific incidents, such as automatically increasing resources during an OutOfMemory event. This proactive approach ensures system stability and reliability.
- Prerequisites
- Kubernetes 1.12+
- Valid storageClass
- Prometheus
- Grafana Loki
- Jaeger
