Multi-objective Joint Optimization of Communication-Computation-Caching Resources in Mobile Edge Computing

Xiaoting Wang, Weijun Cheng, Chenshan Ren
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引用次数: 4

Abstract

With the development of the commercial scale of 5G, the explosive growth of smart mobile devices has promoted the emergence of new applications. How to reasonably design and allocate computing resources and improve users’ experience quality with the limited computing and storage capabilities of mobile devices is a challenging problem. The existing work about joint optimization either minimizes the execution delay or the energy consumption of communication, computation, and caching resources of all the devices. However, the single-objective optimization may not be a practical solution given the heterogeneous capabilities and service requirements of mobile devices. This paper proposes a multi-objective joint optimization of communication-computation-caching resources to satisfy the various devices’ requirements for execution delay and energy consumption. We reformulate to optimize the tradeoff between energy consumption and latency with the limited computing and storage resources. Then, the problem is transferred to a multi-objective problem and solved by the multi optimization method of non-dominated sorting genetic algorithm II (NSGA-II). Simulation results demonstrate that the proposed approach can achieve the tradeoff between energy consumption and latency with different practical scenarios.
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移动边缘计算中通信-计算-缓存资源多目标联合优化
随着5G商用规模的发展,智能移动设备的爆发式增长推动了新应用的出现。如何在移动设备有限的计算和存储能力下,合理设计和分配计算资源,提高用户体验质量是一个具有挑战性的问题。现有的联合优化工作要么是最小化执行延迟,要么是最小化所有设备的通信、计算和缓存资源的能耗。然而,考虑到移动设备的异构功能和服务需求,单目标优化可能不是一个实用的解决方案。为了满足不同设备对执行延迟和能耗的要求,提出了一种通信-计算-缓存资源的多目标联合优化方法。在有限的计算和存储资源下,我们重新制定了优化能耗和延迟之间的权衡。然后将该问题转化为多目标问题,采用非支配排序遗传算法II (NSGA-II)的多优化方法进行求解。仿真结果表明,该方法可以在不同的实际场景下实现能耗与时延的平衡。
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