[论文解读] Delay-Energy Joint Optimization for Task Offloading in Mobile Edge Computing

Zhuang Wang, Weifa Liang+2 more|arXiv (Cornell University)|Apr 27, 2018

IoT and Edge/Fog Computing参考文献 11被引用 9

一句话总结

本文提出了一种用于移动边缘计算（MEC）任务卸载的延迟-能耗联合优化框架，联合优化移动设备的CPU周期频率调节、服务器选择及任务分配，以在满足延迟约束的前提下最小化能耗。该方法将混合整数非线性规划（MINLP）松弛为可多项式时间求解的非线性规划（NLP），并从松弛解中导出可行解，在仿真中相较于基线方法表现出更优性能。

ABSTRACT

Mobile-edge computing (MEC) has been envisioned as a promising paradigm to meet ever-increasing resource demands of mobile users, prolong battery lives of mobile devices, and shorten request response delays experienced by users. An MEC environment consists of many MEC servers and ubiquitous access points interconnected into an edge cloud network. Mobile users can offload their computing-intensive tasks to one or multiple MEC servers for execution to save their batteries. Due to large numbers of MEC servers deployed in MEC, selecting a subset of servers to serve user tasks while satisfying delay requirements of their users is challenging. In this paper, we formulate a novel delay-energy joint optimization problem through jointly considering the CPU-cycle frequency scheduling at mobile devices, server selection to serve user offloading tasks, and task allocations to the selected servers. To this end, we first formulate the problem as a mixed-integer nonlinear programming, due to the hardness to solve this nonlinear programming, we instead then relax the problem into a nonlinear programming problem that can be solved in polynomial time. We also show how to derive a feasible solution to the original problem from the solution of this relaxed solution. We finally conduct experiments to evaluate the performance of the proposed algorithm. Experimental results demonstrate that the proposed algorithm is promising.

研究动机与目标

• 解决移动边缘计算（MEC）环境中最小化能耗与响应延迟的挑战。
• 联合优化卸载任务的CPU周期频率调度、服务器选择与任务分配。
• 为延迟-能耗联合优化问题建立混合整数非线性规划（MINLP）模型。
• 将MINLP松弛为可多项式时间求解的非线性规划，以实现高效计算。
• 从松弛解中导出原问题的可行解。

提出的方法

• 将任务卸载问题建模为考虑能耗与延迟权衡的混合整数非线性规划（MINLP）。
• 将MINLP松弛为非线性规划（NLP）问题，以实现多项式时间求解。
• 应用Jensen不等式与凸优化技术推导边界并确保解的可行性。
• 采用动态电压与频率调节（DVFS）以优化移动设备的能耗。
• 通过取整与分配调整，从松弛的NLP解中导出原MINLP问题的可行解。
• 采用基于仿真的评估方法，使用如服务器数量（m）和任务权重因子（α）等参数验证性能。

实验结果

研究问题

• RQ1 如何在移动边缘计算任务卸载中联合最小化能耗与处理延迟？
• RQ2 本地执行与向多个MEC服务器卸载之间的最优权衡是什么？
• RQ3 所选MEC服务器数量如何影响整体延迟与能耗？
• RQ4 用于平衡延迟与能耗的权重因子α如何影响系统性能？
• RQ5 松弛的非线性规划解能否有效转化为原离散优化问题的可行解？

主要发现

• 所提出的算法（TOS）在所有评估算法中整体延迟最低，尤其在所选服务器数量较少时（m ≤ 8）表现更优。
• 当m ≤ 8时，TOS因本地处理增加而产生更高能耗，但随着m超过8后，该影响逐渐减弱。
• 增加权重因子α可将整体延迟降低至α = 70；超过此值后，延迟进一步降低的影响可忽略不计。
• 随着α增加，TOS与Local_Execution的能耗均上升，表明延迟最小化与能耗效率之间存在权衡。
• 所提算法在延迟与能耗效率方面均优于基线方法（Local_Execution、MEC_Execution、Mixed_Execution），实验结果证实其性能优于理论估计。
• 松弛方法成功将复杂的MINLP转化为可处理的NLP，且所导出的可行解保持优异性能，验证了该方法论的有效性。