Task Optimization Allocation in Vehicle Based Edge Computing Systems With Deep Reinforcement Learning

With the recent advancement in network technologies, the vehicle based medical networks extend medical services to mobile vehicles, thereby offering flexible and efficient healthcare services for vehicle users in need. The integration of vehicle based medical network and edge computing enables computation intensive medical service tasks to be offloaded on edge servers, to provide fast service response for vehicle users. An efficient task offloading and resource allocation strategy is critical for Vehicle based Medical Edge Computing System (VMECS) to satisfy real-time and reliability requirements while ensuring service performance. To this end, in this paper, we investigate the problem of task computation allocation in VMECS networks. By introducing deep reinforcement learning, we first present a novel VMECS architecture to automatically achieve the optimal task offloading and resource allocation through the multi-agent collaboration, thereby improving service performance. Then, we formulate the problem of task offloading and resource allocation in VMECS networks as an optimization model with the aim of maximizing task success rate by jointly considering communication interferences, resource allocation and delay requirements. To solve it, we further devise a Distributed distributional deterministic policy gradients based Task offloading and Resource allocation (DTR) algorithm. Final simulation results demonstrate that compared with benchmark algorithms, DTR algorithm can obtain higher task success rate, smaller service time, and less task processing time.