Task Scheduling Based on Adaptive Priority Experience Replay on Cloud Platforms

Abstract

Task scheduling algorithms based on reinforce learning (RL) have been important methods with which to improve the performance of cloud platforms; however, due to the dynamics and complexity of the cloud environment, the action space has a very high dimension. This not only makes agent training difficult but also affects scheduling performance. In order to guide an agent’s behavior and reduce the number of episodes by using historical records, a task scheduling algorithm based on adaptive priority experience replay (APER) is proposed. APER uses performance metrics as scheduling and sampling optimization objectives with which to improve network accuracy. Combined with prioritized experience replay (PER), an agent can decide how to use experiences. Moreover, this algorithm also considers whether a subtask is executed in a workflow to improve scheduling efficiency. Experimental results on Tpc-h, Alibaba cluster data, and scientific workflows show that a model with APER has significant benefits in terms of convergence and performance.