Autonomous Optimization for Kubernetes Applications and Clusters
Update: 2024-02-11
Description
00:22 .01 Introduction to Speaker and Session
01:18 .50 Understanding Request and Limits in Kubernetes
02:35 .48 Understanding CFS Shares and Quota
05:52 .56 Best Practices for Setting Resources
07:03 .11 Challenges in Managing Kubernetes
08:54 .54 Auto Scaling Solutions in Kubernetes
10:07 .11 Complexities Requiring Machine Learning and Autonomous Systems
12:01 .14 Comparison of Tools and Approaches in the Industry
14:14 .77 Rightsizing Workloads and Performance Optimization
20:50 .09 Node Optimization and Selection
23:26 .55 Monitoring-based Optimization
24:37 .05 Application Performance and Memory Optimization
26:14 .41 Cost Reduction through Workload Optimization
28:23 .65 Hybrid Approach for Predictive and Reactive Scaling
31:12 .24 AI Engines and Anomaly Detection
33:23 .41 Autonomous Approach in Kubernetes
01:18 .50 Understanding Request and Limits in Kubernetes
02:35 .48 Understanding CFS Shares and Quota
05:52 .56 Best Practices for Setting Resources
07:03 .11 Challenges in Managing Kubernetes
08:54 .54 Auto Scaling Solutions in Kubernetes
10:07 .11 Complexities Requiring Machine Learning and Autonomous Systems
12:01 .14 Comparison of Tools and Approaches in the Industry
14:14 .77 Rightsizing Workloads and Performance Optimization
20:50 .09 Node Optimization and Selection
23:26 .55 Monitoring-based Optimization
24:37 .05 Application Performance and Memory Optimization
26:14 .41 Cost Reduction through Workload Optimization
28:23 .65 Hybrid Approach for Predictive and Reactive Scaling
31:12 .24 AI Engines and Anomaly Detection
33:23 .41 Autonomous Approach in Kubernetes
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