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Evaluation of Kubernetes-based WAO
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Classroom Contents
A K8s-Based Workload Allocation Optimizer for Minimizing Power Consumption
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- 1 Intro
- 2 Who are we: Matsuoka Lab at Osaka Univ.
- 3 Our technology: Liquid immersion cooling
- 4 Our technology: optimal task allocation
- 5 The complexity of edge computing systems
- 6 Spread of Microservices & Power consumption increases
- 7 Our Approach: WAO on K8s
- 8 WAO power saving operation strategy
- 9 Scheduler Framework on K8s
- 10 Architecture of WAO Based Scheduler
- 11 Architecture of WAO Based Load Balancer
- 12 Setting of Machine Learning Power consumption (PC) model
- 13 How does it work? How is the performance? Environment
- 14 Preset Temperature of Air Conditioner
- 15 Power Consumption Model
- 16 Evaluation Value in WAO Based Load Balancer
- 17 Evaluation of Power Consumption Reduction
- 18 Evaluation of "WAO Scheduler" + MetalLB
- 19 Evaluation of Kube Scheduler + "WAOLB"
- 20 Evaluation of complete K8s-WAO solution
- 21 Evaluation of Kubernetes-based WAO
- 22 Evaluation of Response Time
- 23 Conclusion
