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Temporal Analysis

The Temporal Analysis view provides insights into time-based trends and behaviors in your Kubernetes cluster.

Overview

This analysis focuses on:

  • Time Series Analysis: Metrics collected over time
  • Behavioral Patterns: Weekly/daily patterns in cluster usage
  • Temporal Anomalies: Short-term abnormal behaviors
  • Trend Forecasting: Long-term trends prediction

Key Components

Time Series Metrics

  • CPU and memory usage over time
  • Network traffic patterns
  • Persistent storage usage
  • Pod creation and deletion rates
  • Daily Patterns: Usage starts, peaks, and ends
  • Example: Increased usage during work hours
  • Weekly Trends: Weekly recurring patterns
  • Example: Increased weekend batch processing

Anomaly Detection

  • Sudden spikes in usage
  • Unexpected drops in resource consumption
  • Anomalies in pod lifecycle events

Forecasting and Predictions

  • CPU and memory usage forecasting
  • Storage needs prediction
  • Network demand forecasts

Usage Examples

# Perform temporal analysis
k8s-analyzer analyze --view temporal-analysis

# Focus on specific metrics
k8s-analyzer analyze --view temporal-analysis --metrics cpu,network

# Create trend forecast reports
k8s-analyzer forecast --output report.csv

Integration

Monitoring Tools

  • Prometheus for long-term metrics storage
  • Grafana for time series visualization
  • ML algorithms for anomaly detection and prediction

Automating Forecasts

  • CI/CD pipeline integration for scheduled forecasts
  • Integration with resource management for proactive scaling
  • Alerts based on trend deviations and anomalies

Best Practices

Metrics Collection

  1. Granular Data: Collect detailed metrics for accuracy
  2. Diverse Metrics: Cover CPU, memory, network, and storage
  3. Long-Term Retention: Store historical data for trend identification

Data Usage

  1. Visualize: Use dashboards for real-time insights
  2. Automate: Utilize CI/CD for trend-based decisions
  3. Analyze: Regularly review and adapt resource allocations