Dependent Task-Offloading Strategy Based on Deep Reinforcement Learning in Mobile Edge Computing

Abstract

In mobile edge computing, there are usually relevant dependencies between different tasks, and traditional algorithms are inefficient in solving dependent task-offloading problems and neglect the impact of the dynamic change of the channel on the offloading strategy. To solve the offloading problem of dependent tasks in a dynamic network environment, this paper establishes the dependent task model as a directed acyclic graph. A Dependent Task-Offloading Strategy (DTOS) based on deep reinforcement learning is proposed with minimizing the weighted sum of delay and energy consumption of network services as the optimization objective. DTOS transforms the dependent task offloading into an optimal policy problem under Markov decision processes. Multiple parallel deep neural networks (DNNs) are used to generate offloading decisions, cache the optimal decisions for each round, and then optimize the DNN parameters using priority experience replay mechanism to extract valuable experiences. DTOS introduces a penalty mechanism to obtain the optimal task-offloading decisions, which is triggered if the service energy consumption or service delay exceeds the threshold. The experimental results show that the algorithm produces better offloading decisions than existing algorithms, can effectively reduce the delay and energy consumption of network services, and can self-adapt to the changing network environment.