Social Attribute Aware Task Scheduling Strategy in Edge Computing

Unbalanced load on the edge computing server will seriously affect service capabilities, a task scheduling strategy Reinforced Q-learning-Automatic Intent Picking (RQ-AIP) for edge computing scenarios is proposed. Firstly, the load balance of the entire network is measured based on the load distribution of the server. By combining the reinforcement learning method, the appropriate edge server is matched for the task to meet the resource differentiation needs of sensor node tasks. Then, a mapping relationship between task delay and terminal transmit power is constructed to satisfy the constraints of the physical domain. Combining the social attributes of terminal, the appropriate relay terminal is continuously selected for the task to achieve the load balancing of network by terminal-assisted scheduling. Simulation results show that compared with other load balancing strategies, the proposed strategy can effectively alleviate the load between the edge servers and the traffic of the core network, reduce task processing latency.