A Deep Reinforcement Learning Approach for Online Computation Offloading in Mobile Edge Computing

With the explosion of mobile smart devices, many computation intensive applications have emerged, such as interactive gaming and augmented reality. Mobile edge computing is put forward, as an extension of cloud computing, to meet the low-latency requirements of the applications. In this paper, we consider an edge computing system built in an ultra-dense network with numerous base stations, and heterogeneous computation tasks are successively generated on a smart device moving in the network. An optimal task offloading strategy, as well as optimal CPU frequency and transmit power scheduling, is desired by the device user, to minimize both task completion latency and energy consumption in a long-term. However, due to the stochastic computation tasks and dynamic network conditions, the problem is particularly difficult to solve. Inspired by reinforcement learning, we transform the problem into a Markov decision process. Then, we propose an online offloading approach based on a double deep Q network, in which a specific neural network model is also provided to estimate the cumulative reward achieved by each action. We also conduct extensive simulations to compare the performance of our proposed approach with baselines.