论文标题
延迟最小化，最佳的工作负载分布和雾计算功率控制
Latency Minimization with Optimum Workload Distribution and Power Control for Fog Computing
论文作者
论文摘要
本文研究了一个三层iot-pog-cloud计算系统，以确定每一层的最佳工作负载和功率分配。目的是最大程度地减少具有单个功率约束的最大每层延迟（包括数据处理和传输延迟）。由此产生的最佳资源分配问题是与指数复杂性的混合组合优化问题。因此，该问题首先在适当的建模假设下放松，然后提出了一种有效的迭代方法来解决放松但仍然非凸的问题。所提出的算法基于一种交替的优化方法，该方法可产生接近最低的结果，并显着降低了复杂性。提供数值结果以说明与详尽搜索方法相比，提出的算法的性能。三层分布式IoT-Fog-Cloud计算的延迟增益是相对于仅雾和仅云的计算系统的。
This paper investigates a three-layer IoT-fog-cloud computing system to determine the optimum workload and power allocation at each layer. The objective is to minimize maximum per-layer latency (including both data processing and transmission delays) with individual power constraints. The resulting optimum resource allocation problem is a mixed-integer optimization problem with exponential complexity. Hence, the problem is first relaxed under appropriate modeling assumptions, and then an efficient iterative method is proposed to solve the relaxed but still non-convex problem. The proposed algorithm is based on an alternating optimization approach, which yields close-to-optimum results with significantly reduced complexity. Numerical results are provided to illustrate the performance of the proposed algorithm compared to the exhaustive search method. The latency gain of three-layer distributed IoT-fog-cloud computing is quantified with respect to fog-only and cloud-only computing systems.
