异构云环境下AHP定权的多目标强化学习作业调度方法

随着新型基础设施建设(新基建)的加速,云计算将获得新的发展契机.数据中心作为云计算的基础设施,其内部服务器不断升级换代,这造成计算资源的异构化.如何在异构云环境下,对作业进行高效调度是当前的研究热点之一.针对异构云环境多目标优化调度问题,设计一种AHP定权的多目标强化学习作业调度方法.首先定义执行时间、平台运行能耗、成...

Multi-objective reinforcement learning job scheduling method using AHP fixed weight in heterogeneous cloud environment

With the acceleration of new infrastructure, cloud computing will be given an entirely new opportunity to develop. Data centers, as the infrastructure of cloud computing, theirs internal servers are continuously updated, which leads to the heterogeneity of computing resources. How to efficiently schedule jobs in heterogeneous cloud environment has become an increasingly popular research. This paper designs a multi-objective reinforcement learning job scheduling method using AHP fixed weight in heterogeneous cloud environment. First, we define the execution time, energy consumption, execution cost, etc. Service delay cost is used to describe the user satisfaction for service. Then, a comprehensive evaluation method for multi-objective scheduling is designed. The weight coefficient of each object is determined by the analytic hierarchy process(AHP). The method is introduced into the calculation of rewards, which can reflect the overall situation and serve as an excellent learning tool for the following jobs. The experimental results show that the proposed method can better optimize the execution efficiency, while ensuring the interests of users and service providers.