Task Offloading Based on Application Hit Ratio

Junna Zhang, Xinxin Wang, Chuntao Ding, Xiaoyan Zhao, Shangguang Wang

Abstract: It has become mainstream for mobile devices to offload latency-sensitive applications to edge servers for execution to meet low-latency requirements. However, the existing related studies lack the consideration of application hit ratio, which makes them unable to meet the increasingly complex offloading of multi-applications including multi-tasks. To this end, this paper proposes a Multi-task offloading and Service placement optimization (MSO) method with the goal of maximizing the application hit ratio to provide high-quality service. The proposed MSO is constructed with Improved Multi-Agent Q-Learning (IMAQL) and load-balancing algorithms. IMAQL aims to learn an optimal service placement policy by using Q-learning techniques. Next, the load-balancing algorithm is designed to offload tasks according to the service placement policy. To verify the effectiveness of the MSO method, we conduct extensive experiments on a publicly available dataset. The experimental results show that the proposed method can improve the application hit ratio by appropriately 2.6% to 9% compared with other methods.