Drag-JDEC: A Deep Reinforcement Learning and Graph Neural Network-based Job Dispatching Model in Edge Computing

The emergence of edge computing eases latency pressure in remote cloud and computing pressure of terminal devices, providing new solutions for real-time applications. Jobs of end devices are offloaded to a server in the cloud or an edge cluster for execution. Unreasonable job dispatching strategies will not only affect the completion time of tasks violating the users' QoS but also reduce the resource utilization of servers increasing the operating costs of service providers. In this paper, we propose an online job dispatching model named Drag-JDEC based on deep reinforcement learning and graph neural network. For natural directed acyclic graph-type jobs, we use a graph attention network to aggregate the features of neighbor nodes and transform them into high-dimensional ones. Combining with the current status of edge servers, the deep reinforcement learning module makes the dispatching decision for each task in the job to keep load balancing and meet the users' QoS. Experiments using real job data sets show that Drag-JDEC outperforms traditional and state-of-the-art algorithms for balancing the workload of edge servers and adapts to various edge server configurations well, reaching the maximum improvement of 34.43%.