结合智能电网技术的移动边缘计算系统资源优化配置研究

Mobile Edge Computing (MEC) has been considered as a key enabler for future 5G communication scenarios. Integrating smart-grid technologies into the design of green MEC, this project investigates optimal resource allocation schemes for grid power, renewable energy and computing resources. Leveraging Lyapunov optimization method, we aim to establish a coherent framework of stochastic optimization theory and techniques for resource allocation in smart-grid powered MEC, design efficient resource allocation algorithms with a low complexity on-the-fly, and theoretically analyze and justify that the proposed algorithms can greatly improve the energy efficiency of the MEC system in a practical stochastic environment. Through the theoretical as well as the experimental studies, this project will explore a new paradigm of real-time resource management for smart-grid powered MEC, benefit directly applications to emerging green MEC architecture and system designs, and facilitate the integration of the industries of smart grids and communications.

移动边缘计算（mobile edge computing, MEC）被认为是实现未来5G移动通信场景的关键技术。本项目以高能效绿色MEC系统为切入点，致力提出结合智能电网技术进行电网供电、可再生能源和计算资源优化配置新方案。项目拟围绕李雅普诺夫优化技术，建立一套结合智能电网技术进行绿色MEC系统资源调度的随机优化方法理论和技术体系，设计在线资源调度算法，实现电网供电、可再生能源和计算资源的高效、低复杂度调度，并从理论上分析和证明所提出的算法机制可在实际复杂随机环境下大幅提高MEC系统的能效。通过基础理论的研究辅以实验验证，本项目将深入探索结合智能电网技术进行MEC资源优化配置的新范式和新前沿方向，为新兴的绿色MEC系统设计和建设提供新思路和新技术，促进智能电网产业与通信产业的融合。

本项目以高能效移动边缘计算（MEC）系统为切入点，提出结合智能电网技术进行电网与可再生能源联合供电下的无线与计算资源联合优化配置新方案。项目拟围绕李雅普诺夫优化技术，建立一套结合智能电网技术进行绿色MEC系统资源调度的随机优化方法理论和技术体系，设计在线资源调度算法，实现MEC在混合能源供电下无线与计算资源的高效、低复杂度调度，并从理论上分析和证明所提出的算法机制可在实际复杂随机环境下大幅提高MEC系统的能效。通过基础理论的研究辅以实验验证，本项目探索了结合智能电网技术进行MEC资源优化配置的新范式和新前沿方向，为低碳无线通信系统提供了新思路和新技术。