An Intelligent Affinity Strategy for Dynamic Task Scheduling in Cloud-Edge-End Collaboration
Jingsen Zhang, Shoulu Hou, Yi Gong, Tao Wang, Changyuan Lan, Xiulei Liu · 2024
The cloud-edge-end collaboration framework is emerging as a promising means to handle diverse tasks and improve Quality of Service. Existing research rarely considers the affinity between diverse tasks and heterogeneous resources, preventing the further improvement of system performance. This paper proposes a dynamic task scheduling approach based on the intelligent affinity strategy for cloud-edge-end collaboration. The affinity strategy depicts the preference of tasks for computing nodes with different labels, including resource types and node zones, and each task can set multiple affinity rules to match target nodes. Additionally, the proposed approach adopts deep reinforcement learning theory to generate the affinity rules, which means utilizing an intelligent algorithm to guide the rule generation. Extensive experiments show that the proposed approach can reduce the average cost by at least 20% compared with the baseline.