iRAF: A Deep Reinforcement Learning Approach for Collaborative Mobile Edge Computing IoT Networks

• Published in: IEEE internet of things journal 2019-08, Vol.6 (4), p.7011-7024
• Main Authors: Chen, Jienan, Chen, Siyu, Wang, Qi, Cao, Bin, Feng, Gang, Hu, Jianhao
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial intelligence; Artificial neural networks; Collaboration; Collaborative mobile edge computing (CoMEC); Computer simulation; Deep learning; deep reinforcement learning (DRL); Delays; Edge computing; Electronic devices; intelligent resource allocation framework (iRAF); Internet of Things; Internet of Things (IoT); Machine learning; Mobile computing; Monte Carlo tree search (MCTS); Power consumption; Reinforcement learning; Resource allocation; Resource management; Servers; Task analysis; Training; Wireless networks
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2019.2913162

Recently, as the development of artificial intelligence (AI), data-driven AI methods have shown amazing performance in solving complex problems to support the Internet of Things (IoT) world with massive resource-consuming and delay-sensitive services. In this paper, we propose an intelligent resource allocation framework (iRAF) to solve the complex resource allocation problem for the collaborative mobile edge computing (CoMEC) network. The core of iRAF is a multitask deep reinforcement learning algorithm for making resource allocation decisions based on network states and task characteristics, such as the computing capability of edge servers and devices, communication channel quality, resource utilization, and latency requirement of the services, etc. The proposed iRAF can automatically learn the network environment and generate resource allocation decision to maximize the performance over latency and power consumption with self-play training. iRAF becomes its own teacher: a deep neural network (DNN) is trained to predict iRAF's resource allocation action in a self-supervised learning manner, where the training data is generated from the searching process of Monte Carlo tree search (MCTS) algorithm. A major advantage of MCTS is that it will simulate trajectories into the future, starting from a root state, to obtain a best action by evaluating the reward value. Numerical results show that our proposed iRAF achieves 59.27% and 51.71% improvement on service latency performance compared with the greedy-search and the deep Q -learning-based methods, respectively.