Research on Kubernetes-based Cluster Energy-Saving Strategy
Within Kubernetes,the Horizontal Pod Autoscaler(HPA)possesses automatic Pod-scaling capability,adjusting the number of Pods based on fluctuations in traffic,increasing the Pod count during peak periods to meet demand,and reducing it during off-peak times to conserve resources.However,because HPA scales are based on the current performance metrics of Pods,sudden traffic surges can potentially have detrimental effects on the availability of application services.In addition,during periods of low demand,idle computing resources lead to a waste of resources.To address these challenges,this study investigates and validates cluster resource autoscaling and intelligent sleep-wake strategy based on time-series forecasting.This strategy utilizes the GC-TimesNet model to predict cluster resource usage.When resource utilization is low,the strategy calculates the number of compute nodes that need to be shut down,marks these nodes as unschedulable,evicts existing Pods,and places these machines in a sleep state.Conversely,when the resource demand increases,a sufficient number of machines are awakened,and the HPA controller is used to increase the required number of Pod replicas.The experimental results demonstrate that this strategy can reasonably and accurately predict trends in cluster load changes,enhance the operational management capabilities for optimizing clusters,maximize the utilization of computing resources,provide data support for reducing cluster energy expenses,and achieve energy savings and emission reduction when combined with the implementation of intelligent sleep and wake strategies.
Kubernetes toolcontainer orchestrationcluster energy-savingtime series forecastingGC-TimesNet modelconvolutional neural networkattention mechanismenergy saving and emission reduction
万方数据
维普
