Hyper-Heuristic Task Scheduling Algorithm Based on Reinforcement Learning in Cloud Computing

The solution strategy of the heuristic algorithm is pre-set and has good performance in the conventional cloud resource scheduling process. However, for complex and dynamic cloud service scheduling tasks, due to the difference in service attributes, the solution efficiency of a single strategy is low for such problems. In this paper, we presents a hyper-heuristic algorithm based on reinforcement learning (HHRL) to optimize the completion time of the task sequence. Firstly, In the reward table setting stage of HHRL, we introduce population diversity and integrate maximum time to comprehensively determine the task scheduling and the selection of low-level heuristic strategies. Secondly, a task computational complexity estimation method integrated with linear regression is proposed to influence task scheduling priorities. Besides, we propose a high-quality candidate solution migration method to ensure the continuity and diversity of the solving process. Compared with HHSA, ACO, GA, F-PSO, etc, HHRL can quickly obtain task complexity, select appropriate heuristic strategies for task scheduling, search for the the best makspan and have stronger disturbance detection ability for population diversity.

Automated summary

In this paper, a hyper-heuristic algorithm based on reinforcement learning (HHRL) is proposed to optimize the completion time of the task sequence. The algorithm introduces population diversity in the reward table setting stage and comprehensively considers task scheduling and the selection of low-level heuristic strategies. Furthermore, a task complexity estimation method and a high-quality candidate solution migration method are proposed. Compared with other algorithms, HHRL can quickly obtain task complexity, select appropriate heuristic strategies for task scheduling, and has stronger disturbance detection ability for population diversity.