支持MEC的移动IoT网络中的能量效率和延迟权衡
Energy Efficiency and Delay Tradeoff in an MEC-Enabled Mobile IoT Network
摘要：
移动边缘计算（MEC）最近成为5G时代一项有前途的技术。它被认为是支持计算密集型和延迟关键应用的有效范例，即使在能源受限和计算受限的物联网（IoT）设备上也是如此。为了有效地利用MEC带来的性能优势，必须通过考虑非平稳计算需求、用户移动性和无线衰落信道来联合分配无线电和计算资源。本文旨在研究在用户移动场景中提供卸载服务时，多用户多服务器MEC启用的物联网系统的能效（EE）和服务延迟之间的权衡。特别是，我们制定了一个随机优化问题，目标是最大限度地减少长期平均网络EE的任务队列稳定性，峰值发射功率，最大CPU周期频率和最大用户数的约束。针对该问题，基于李雅普诺夫优化理论，提出了一种在线卸载和资源分配算法。该算法将原问题转化为每个时隙上的若干个独立子问题，然后采用凸分解和子模块法求解。理论分析表明，该算法能够在EE和业务时延之间实现$[O(1/V), O(V)]$折衷。仿真结果验证了理论分析，并表明我们提出的算法可以提供更好的EE延迟性能的任务卸载的挑战，相比几个基线。
英文摘要：
Mobile Edge Computing (MEC) has recently emerged as a promising technology in the 5G era. It is deemed an effective paradigm to support computation-intensive and delay critical applications even at energy-constrained and computation-limited Internet of Things (IoT) devices. To effectively exploit the performance benefits enabled by MEC, it is imperative to jointly allocate radio and computational resources by considering non-stationary computation demands, user mobility, and wireless fading channels. This paper aims to study the tradeoff between energy efficiency (EE) and service delay for multi-user multi-server MEC-enabled IoT systems when provisioning offloading services in a user mobility scenario. Particularly, we formulate a stochastic optimization problem with the objective of minimizing the long-term average network EE with the constraints of the task queue stability, peak transmit power, maximum CPU-cycle frequency, and maximum user number. To tackle the problem, we propose an online offloading and resource allocation algorithm by transforming the original problem into several individual subproblems in each time slot based on Lyapunov optimization theory, which are then solved by convex decomposition and submodular methods. Theoretical analysis proves that the proposed algorithm can achieve a $[O(1/V), O(V)]$ tradeoff between EE and service delay. Simulation results verify the theoretical analysis and demonstrate our proposed algorithm can offer much better EE-delay performance in task offloading challenges, compared to several baselines.
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