Research On Resource Scheduling Strategy Based On Kubernetes Container Cluster

The emergence of container orchestration tools has enabled container virtualization technology to solve many problems such as low utilization of cloud platform resources and slow scheduling and distribution.Therefore,the scheduling algorithm of the container orchestration tool is studied,which can effectively allocate container cluster resources,improve resource utilization,and minimize the total cost of resource consumption.In order to get a better scheduling strategy,the paper uses meta-heuristic algorithm to solve the problems in scheduling strategy of the mainstream container orchestration tool-Kubemetes.For the Kubernetes scheduling model,when the long-term running service is deployed,the resource consumption cost is not considered and the multi-Pod scheduling does not consider the cluster load degree.The ant colony particle swarm optimization algorithm is proposed to optimize the basic scheduling model.First,establish the objective function of cost and cluster load;Then,the node optimization strategy of the basic scheduling model is improved by the node optimization strategy of the ant colony particle swarm algorithm;Finally,four scheduling models are implemented on the CloudSim platform:ant colony,particle swarm,ant colony optimization particle swarm and basic scheduling model.A new preemptive scheduling strategy is proposed for the above four scheduling models with high scheduling failure rate when the cluster resources are insufficient.Firstly,the priority of Pod is designed according to the running state,termination state and restart strategy of Pod.Secondly,a new preemptive scheduling model is designed according to the preemptive scheduling mechanism.Aiming at the problem of time-consuming in ant colony particle swarm algorithm,a parallelization algorithm based on Go scheduling model principle is proposed to optimize node optimization efficiency.Firstly,the Goroutine parallelism is tested on the IntelliJ IDEA platform to find the optimal parallelism;Then,based on the optimal parallelism,the ant colony,ant colony-optimized particle swarm and particle swarm-optimization ant colony algorithmare are designed to parallel the process of node optimization and calculation.Finally,based on the optimized objective function,the experimental comparison between the basic scheduling model and the parallel scheduling model is carried out.Experiments show that the ant colony and ant colony-optimized particle swarm scheduling model designed in this paper not only saves cost,but also reduces the load degree of the cluster when multi-Pod scheduling;The preemptive scheduling policy reduces the scheduling failure rate of the Pod when the cluster resources are insufficient.In the experimental environment of Goroutine optimal parallelism,the running time of the parallel ant colony algorithm is nearly 2 to 3 times shorter than that of the serial ant colony algorithm.And the parallel ant colony scheduling model has the best Scheduling effect compared with other scheduling models.