Online Computation Offloading and Resource Scheduling 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 (EC) is put forward, as an extension of cloud computing, to meet the low-latency requirements of the applications. In this article, we consider an EC system built in an ultradense network with numerous base stations. 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 task generation 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 attention-based double deep Q network (DDQN) approach, in which two neural networks are employed to estimate the cumulative latency and energy rewards achieved by each action. Moreover, a context-aware attention mechanism is designed to adaptively assign different weights to the values of each action. We also conduct extensive simulations to compare the performance of our proposed approach with several heuristic and DDQN-based baselines.

I. Introduction

With the proliferation of mobile smart devices, many novel applications applied to mobile clients have emerged, such as online interactive games, video stream analysis, and augmented reality [1]–[3]. These applications with intensive computation demands always ask for low completion latency, which cannot be satisfied by mobile devices due to their limited computing resources. Benefit from the development of wireless communications [4], such as 5G and edge computing (EC) [5], [6], has been proposed as a promising paradigm to provide computing service at the edge of core networks, which is in close proximity to mobile devices.