Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks
By Liang Huang, Suzhi Bi, Ying-Jun Angela Zhang. IEEE Transactions on Mobile Computing, 2018
708 citations recorded by the source corpus.
Wireless powered mobile-edge computing (MEC) has recently emerged as a promising paradigm to enhance the data processing capability of low-power networks, such as wireless sensor networks and internet of things (IoT). In this paper, we consider a wireless powered MEC network that adopts a binary offloading policy, so that each computation task of wireless devices (WDs) is either executed locally or fully offloaded to an MEC server. Our goal is to acquire an online algorithm that optimally adapts task offloading decisions and wireless resource allocations to the time-varying wireless channel conditions. This requires quickly solving hard combinatorial optimization problems within the channel coherence time, which is hardly achievable with conventional numerical optimization methods. To tackle this problem, we propose a Deep Reinforcement learning-based Online Offloading (DROO) framework that implements a deep neural network as a scalable solution that learns the binary offloading decisions from the experience. It eliminates the need of solving combinatorial optimization problems, and thus greatly reduces the computational complexity especially in large-size networks. To further reduce the complexity, we propose an adaptive procedure that automatically adjusts the parameters of the DROO algorithm on the fly. Numerical results show that the proposed algorithm can achieve near-optimal performance while significantly decreasing the computation time by more than an order of magnitude compared with existing optimization methods. For example, the CPU execution latency of DROO is less than 0.1 second in a 30-user network, making real-time and optimal offloading truly viable even in a fast fading environment.
Subfields: Computer Networks and Communications, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/abe45d6ddb495d5756f2b7658a7cf620226a2e51
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Citations in the corpus
- Fast Adaptive Task Offloading in Edge Computing Based on Meta Reinforcement Learning (2020, background): “The task offloading problem in MEC has attracted significant research interests [5], [10], [11], [13], [29], [30], [31], [32], Fig.”
- AutoScale: Energy Efficiency Optimization for Stochastic Edge Inference Using Reinforcement Learning (2020, methodology): “Various offloading techniques that execute all or some computations on remote servers while considering the variability of the mobile environment have been also proposed [1], [2], [16], [48], [56], [61], [87], [91], [113], [117].”
- Deep Reinforcement Learning for Task Offloading in Mobile Edge Computing Systems (2020, methodology): “Different from those works [17]–[20], we aim to propose a DRL-based distributed algorithm that can address the unknown load dynamics at the edge nodes.”
- A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art (2019, neutral): “a continuous control with DRL based framework of computation offloading and resource allocation in wireless powered MEC systems was studied in [249].”