Kubernetes Resource Scheduling Strategy for 5G Edge Computing

Abstract

Container cloud is a key supporting technology for 5G edge computing,but edge computing faces great resource pressure imposed by the large bandwidth,low latency,and massive connections of 5G.The scheduler of container cloud,Kubernetes,only collects remaining CPU and memory of nodes,and uses a fixed weight to calculate the priority of nodes as the basis of scheduling.The mechanism cannot meet the demand for refined resource scheduling in edge computing scenarios.To addresses the resource scheduling needs of 5G edge computing,this paper expands the resource scheduling evaluation indicators of Kubernetes,adds bandwidth and disk evaluation indicators to filter and select nodes,and on this basis proposes a scheduling mechanism named WSLB,which realizes weight self-learning based on resource occupancy.WSLB dynamically calculates the resource weight set of the application according to its resource utilization during the running process to enable the weight set to dynamically and adaptively adjust itself based on the size of application traffic.The resource weight set obtained from dynamic learning is used to calculate the priority of candidate nodes,and the node with the highest priority is selected for deployment.Experimental results show that compared with the native scheduling strategy of Kubernetes,WSLB fully considers the bandwidth and disk requirements of edge applications,and avoids deploying applications to nodes where resources are all occupied.In the heavy load and heterogeneous request scenario,the balance of cluster resources under the WSLB mechanism is increased by 10%,the comprehensive utilization rate of resources increased by 2%.