Combining neural network-based method with heuristic policy for optimal task scheduling in hierarchical edge cloud

Deploying service nodes hierarchically at the edge of the network can effectively improve the service quality of offloaded task requests and increase the utilization of resources. In this paper, we study the task scheduling problem in the hierarchically deployed edge cloud. We first formulate the minimization of the service time of scheduled tasks in edge cloud as a combinatorial optimization problem, blue and then prove the NP-hardness of the problem. Different from the existing work that mostly designs heuristic approximation-based algorithms or policies to make scheduling decision, we propose a newly designed scheduling policy, named Joint Neural Network and Heuristic Scheduling (JNNHSP), which combines a neural network-based method with a heuristic based solution. JNNHSP takes the Sequence-to-Sequence (Seq2Seq) model trained by Reinforcement Learning (RL) as the primary policy and adopts the heuristic algorithm as the auxiliary policy to obtain the scheduling solution, thereby achieving a good balance between the quality and the efficiency of the scheduling solution. In-depth experiments show that compared with a variety of related policies and optimization solvers, JNNHSP can achieve better performance in terms of scheduling error ratio, the degree to which the policy is affected by re-sources limitations, average service latency, and execution efficiency in a typical hierarchical edge cloud.