A Bilevel Optimization Approach for Joint Offloading Decision and Resource Allocation in Cooperative Mobile Edge Computing

IF 9.4 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS IEEE Transactions on Cybernetics Pub Date : 2019-06-19 DOI:10.1109/TCYB.2019.2916728
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引用次数: 63

Abstract

This paper studies a multiuser cooperative mobile edge computing offloading (called CoMECO) system in a multiuser interference environment, in which delay-sensitive tasks may be executed on local devices, cooperative devices, or the primary MEC server. In this system, we jointly optimize the offloading decision and computation resource allocation for minimizing the total energy consumption of all mobile users under the delay constraint. If this problem is solved directly, the offloading decision and computation resource allocation are generally generated separately at the same time. Note, however, that they are closely coupled. Therefore, under this condition, their dependency is not well considered, thus leading to poor performance. We transform this problem into a bilevel optimization problem, in which the offloading decision is generated in the upper level, and then the optimal allocation of computation resources is obtained in the lower level based on the given offloading decision. In this way, the dependency between the offloading decision and computation resource allocation can be fully taken into account. Subsequently, a bilevel optimization approach, called BiJOR, is proposed. In BiJOR, candidate modes are first pruned to reduce the number of infeasible offloading decisions. Afterward, the upper-level optimization problem is solved by ant colony system (ACS). Furthermore, a sorting strategy is incorporated into ACS to construct feasible offloading decisions with a higher probability and a local search operator is designed in ACS to accelerate the convergence. For the lower-level optimization problem, it is solved by the monotonic optimization method. In addition, BiJOR is extended to deal with a complex scenario with the channel selection. Extensive experiments are carried out to investigate the performance of BiJOR on two sets of instances with up to 400 mobile users. The experimental results demonstrate the effectiveness of BiJOR and the superiority of the CoMECO system.
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协同移动边缘计算中联合卸载决策和资源分配的双层优化方法
本文研究了一种在多用户干扰环境中的多用户协作移动边缘计算卸载(称为CoMECO)系统,在该系统中,延迟敏感任务可以在本地设备、协作设备或主MEC服务器上执行。在该系统中,我们联合优化了卸载决策和计算资源分配,以在延迟约束下最小化所有移动用户的总能耗。如果直接解决这个问题,卸载决策和计算资源分配通常会同时单独生成。然而,请注意,它们是紧密耦合的。因此,在这种情况下,它们的依赖性没有得到很好的考虑,从而导致性能较差。我们将该问题转化为双层优化问题,在双层优化问题中,在上层生成卸载决策,然后在下层基于给定的卸载决策获得计算资源的最优分配。通过这种方式,可以充分考虑卸载决策和计算资源分配之间的依赖性。随后,提出了一种称为BiJOR的双层优化方法。在BiJOR中,首先对候选模式进行修剪,以减少不可行的卸载决策的数量。然后,利用蚁群系统求解上层优化问题。此外,在ACS中引入了排序策略,以构建具有更高概率的可行卸载决策,并在ACS中设计了局部搜索算子,以加速收敛。对于较低层次的优化问题,采用单调优化方法进行求解。此外,对BiJOR进行了扩展,以处理信道选择的复杂场景。为了研究BiJOR在多达400个移动用户的两组实例上的性能,进行了大量实验。实验结果证明了BiJOR的有效性和CoMECO系统的优越性。
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来源期刊
IEEE Transactions on Cybernetics
IEEE Transactions on Cybernetics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, CYBERNETICS
CiteScore
25.40
自引率
11.00%
发文量
1869
期刊介绍: The scope of the IEEE Transactions on Cybernetics includes computational approaches to the field of cybernetics. Specifically, the transactions welcomes papers on communication and control across machines or machine, human, and organizations. The scope includes such areas as computational intelligence, computer vision, neural networks, genetic algorithms, machine learning, fuzzy systems, cognitive systems, decision making, and robotics, to the extent that they contribute to the theme of cybernetics or demonstrate an application of cybernetics principles.
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