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Title	Mobility-Aware Computation Offloading With Load Balancing In Smart City Networks Using Mec Federation
ID_Doc	37381
Authors	Huang H.; Zhan W.; Min G.; Duan Z.; Peng K.
Year	2024
Published	IEEE Transactions on Mobile Computing, 23, 11
DOI	http://dx.doi.org/10.1109/TMC.2024.3376377
Abstract	Internet-of-Things (IoT) has played a critical role in developing sustainable smart cities and emerging numerous latency-sensitive IoT applications. Mobile edge computing (MEC) federation has the capability to incorporate a transparent resource management approach, which enables the sharing and utilization of MEC services from edge infrastructure providers (EIPs) and provides agile access services to mobile devices (MDs). In this paper, we investigate the joint optimization problem of computation offloading, task migration, and resource allocation in the MEC federation. The objective is to minimize the weighted sum of latency and energy consumption while maintaining load balancing under the constraint of the long-term migration cost budget of EIPs. To address the problem, we decompose it into two sub-problems: 1) the MDs clustering sub-problem and 2) the sub-problem of joint computation offloading, task migration, and resource allocation. First, an MDs clustering matching (MDCM) algorithm is proposed to cluster the MDs in edge servers (ESs) according to the differences in channel gains. Afterward, the second sub-problem is simplified by the Lyapunov optimization technique, and then we propose a Transformer-based mobility prediction model and a decentralized deep deterministic policy gradient (DDPG)-based framework to solve it. Extensive simulation results demonstrate the cost-efficiency of the proposed algorithm. © 2024 IEEE.
Author Keywords	Computation offloading; deep reinforcement learning; mobile edge computing; resource allocation; task migration

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