论文标题
移动边缘计算系统的冷却感知资源分配和负载管理
Cooling-Aware Resource Allocation and Load Management for Mobile Edge Computing Systems
论文作者
论文摘要
在物联网（IoT）的爆炸性计算需求的驱动下，移动边缘计算（MEC）提供了一种有前途的技术来增强移动用户的计算能力。在本文中，我们通过共同优化WPT，本地/边缘计算负载，卸载时间以及中央处理单元（CPU）（CPUS）在接入点（AP）和用户的频率，在具有无线功率传输（WPT）的MEC系统中提出了一种联合资源分配和负载管理机制。为了实现节能和可持续的WPT-MEC系统，我们在满足计算潜伏期要求的同时最大程度地减少了AP的总能源消耗。在最小化MEC系统的能源消耗时，考虑了不可忽略的冷却能。通过严格策划最先进的优化技术，我们设计了一种迭代算法并以半锁定形式获得最佳解决方案。基于解决方案，总结了有趣的属性和见解。广泛的数值测试表明，拟议的算法可节省高达90.4％的现有基准能量。
Driven by explosive computation demands of Internet of Things (IoT), mobile edge computing (MEC) provides a promising technique to enhance the computation capability for mobile users. In this paper, we propose a joint resource allocation and load management mechanism in an MEC system with wireless power transfer (WPT), by jointly optimizing the transmit power for WPT, the local/edge computing load, the offloading time, and the frequencies of the central processing units (CPUs) at the access point (AP) and the users. To achieve an energy-efficient and sustainable WPT-MEC system, we minimize the total energy consumption of the AP, while meeting computation latency requirements. Cooling energy which is non-negligible, is taken into account in minimizing the energy consumption of the MEC system. By rigorously orchestrating the state-of-the-art optimization techniques, we design an iterative algorithm and obtain the optimal solution in a semi-closed form. Based on the solution, interesting properties and insights are summarized. Extensive numerical tests show that the proposed algorithm can save up to 90.4% the energy of existing benchmarks.
