Hybrid Edge-Cloud Collaborator Resource Scheduling Approach Based on Deep Reinforcement Learning and Multiobjective Optimization

Collaborative resource scheduling between edge terminals and cloud centers is regarded as a promising means of effectively completing computing tasks and enhancing quality of service.In this paper, to further improve the achievable performance, the edge cloud resource scheduling (ECRS) problem is transformed into a multi-objective Markov decision process based on task dependency and features extraction.A multi-objective ECRS model is proposed by considering the task completion time, cost, energy consumption and system reliability as the four objectives.Furthermore, a hybrid approach based on deep reinforcement learning (DRL) and multi-objective optimization are employed in our work.Specifically, DRL preprocesses the workflow, and a multi-objective optimization method strives to find the Pareto-optimal workflow scheduling decision.Various experiments are performed on three real data sets with different numbers of tasks.The results obtained demonstrate that the proposed hybrid DRL and multi-objective optimization design outperforms existing design approaches.