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Inverse order based optimization method for task offloading and resource allocation in mobile edge computing
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AI
DOI：10.1016/j.asoc.2021.108361.png)
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Abstract
En 中文
Edge computing, which provides lightweight cloud computing and storage capabilities at the edge of the network, has become a new computing paradigm. A key research challenge for edge computing is to design an efficient offloading strategy for offloading decision-making and resource allocation. Although many researches attempt to address this challenge, the traditional offloading strategies cannot adapt to complex environments, and the offloading strategies based on reinforcement learning require centralized control or the pursuit of the user's best interests, which is impractical. Individual users should rationally pursue benefits in order to create a high-quality offloading environment to obtain long-term benefits. In this paper, we first separate the offloading process into a two-step offloading framework, and reverse the order of solving offloading decision and resource allocation problems to reduce the dimensionality of the action and state space. We formulate the resource allocation as a Markov Decision Process (MDP) and use the Deep Deterministic Policy Gradient Algorithm (DDPG) to adjust load balancing of the edge server and reduce the transmission energy and delay, and then use the genetic algorithm (GA) to search for decisions and use Fully-Connected Network (FCN) to fit the decision-making process, thereby avoiding excessive response time caused by iteration. Simulation results show that compared with baseline methods, the proposed algorithm is more stable, flexible, adaptable and suitable for practical applications. (C) 2021 Elsevier B.V. All rights reserved.
Keywords:
Mobile edge computing
DDPG
GA
Partial offloading
Computation offloading