基于计算吞吐量最大化的能量采集边缘计算系统在线资源优化配置

An Online Resource Allocation Design for Computation Capacity Maximization in Energy Harvesting Mobile Edge Computing Systems

在基于可再生能量收集技术的移动边缘计算(Mobile Edge Computing, MEC)系统中,可再生能量到达和计算卸载无线信道呈现较强的时空变化特性,因此该系统的无线及计算资源管理与用户任务计算之间存在着动态适配的挑战。针对此类问题,本文研究多时隙多用户的能量采集边缘计算系统,建立可再生能量随机到达和无线信道模型以及预测误差模型,以系统总计算吞吐量最大化为准则,通过逐时隙联合优化用户本地计算和计算卸载模块,提出了一种在线滑动窗设计方案,需要通过调整滑动窗长度M来实现。该方案逐时隙求解凸优化问题,基于离线资源动态管控的最优结构,实时制定资源管理策略,具有较低的计算复杂度。仿真实验结果表明,提出的在线滑动窗设计方案在系统计算吞吐量性能方面优于已有的基准方案,并在对抗信道/能量状态信息预测误差方面有较好的鲁棒性能。

In the energy harvesting based mobile edge computing(MEC) system, the energy arrivals and wireless channels for computing offloading are both dynamically changing in time and space, which results in dynamic adaptation between communication/computational resource management and task execution. To address such problems, based on the criterion of maximizing the system’s computing throughput, the predication models for renewable energy random arrival and wireless channel are established, and a novel online design framework is proposed for dynamically managing communication/computation resources over time. This solution solves the convex optimization problem time slot by time slot, and based on the optimal structure of offline resource dynamic management and control, real-time resource management strategies are formulated, and it has low computational complexity. Numerical results show that the proposed online sliding window design scheme is superior to the existing benchmark schemes in terms of system computational throughput performance, and has better robust performance against channel/energy state information prediction errors.