Aiming at the problems of insufficient terminal computing power,limited resources and large delay in edge computing,a collaborative service offloading approach for Internet of Things based on deep reinforcement learning was proposed.The delay model was established through three different offloading methods,and the association relationship between services was mined.The associ-ated services were cooperatively offloaded,and the communication delay of the associated services was added to establish a perfect offloading delay model,and the value of the offload rate and how the associated services were cooperatively offloaded to minimize the delay was combined with the overall model.Therefore,the service request delay and the communication delay between services were minimized.The experiment results showed that our approach could reduce about 20%service delay in the system than other baseline algorithms on searching the optimal service offloading strategy.
维普
万方数据