Multi-agent Cooperative Computing Resource Scheduling Algorithm for Periodic Task Scenarios.

The scheduling of large-scale service requests and jobs usually requires the service cluster to fully use node computing resources. However, due to the increasing number of server devices, the dependence between resource allocation and request, and the periodic external request received, the scheduling process of edge-oriented service requests is a complicated scientific problem. Existing studies do not take into account the periodic characteristics of service requests in different periods, leading to inaccurate scheduling decisions on external requests. This paper proposes a coordinated Multi-Agent recurrent Actor-Critic, based on a recursive network. CMARAC is used to solve the problem of computing resource allocation for periodic requests in edge computing scenarios. According to different resource information in the server cluster and the status of the task queue, the system state information and historical information are captured and maintained by integrating LSTM, and then the most appropriate service resources are selected by processing them in the Actor-Critic network. Tracking experiments using actual request data show that CMARAC can successfully learn the periodic state between external requests in the face of large-scale service requests. Compared with the baseline, the average throughput rate of the system implemented by CMARAC is improved by 2.1%, and the algorithm convergence rate is improved by 0.69 times. Finally, we optimized the parameters through experiments and determined the best parameter configuration of CMARAC.