Distributed Threshold-Based Offloading for Heterogeneous Mobile Edge Computing
Distributed Threshold-Based Offloading for Heterogeneous Mobile Edge Computing
复制标题
DOI：
10.1109/icdcs57875.2023.00024
复制
发表时间：
2023-07
期刊：
影响因子：
--
通讯作者：
Xu-Zhen Qin;Qiaomin Xie;Bin Li
中科院分区：
文献类型：
--
作者：
Xu-Zhen Qin;Qiaomin Xie;Bin Li
在本文中，我们考虑一个大规模异构移动边缘计算系统，其中每个设备的平均计算任务到达率、平均服务率、平均能耗和平均卸载延迟是从不同的有界连续概率分布中得出的，以反映不同的计算密集型应用、具有不同计算能力和电池效率的移动设备以及不同类型的无线接入网络（例如4G/SG蜂窝网络、WiFi）。我们考虑一类基于分布式阈值的随机卸载策略，并根据其计算负载、平均卸载延迟、平均能耗和边缘服务器处理时间（取决于服务器利用率）开发阈值更新算法。我们证明，当移动设备的任务处理时间服从指数分布时，在大系统极限下始终存在唯一的平均场纳什均衡（MFNE）。这是通过仔细划分平均到达率空间以考虑每个设备最佳阈值的离散结构来实现的。此外，我们还表明我们提出的阈值更新算法收敛于 MFNE。最后，我们进行模拟来证实我们的理论结果，并证明我们提出的算法在基于收集的现实世界数据的更通用的设置中仍然表现良好，并且优于众所周知的概率卸载策略。
In this paper, we consider a large-scale heterogeneous mobile edge computing system, where each device's mean computing task arrival rate, mean service rate, mean energy consumption, and mean offloading latency are drawn from different bounded continuous probability distributions to reflect the diverse compute-intensive applications, mobile devices with different computing capabilities and battery efficiencies, and different types of wireless access networks (e.g., 4G/SG cellular networks, WiFi). We consider a class of distributed threshold-based randomized offloading policies and develop a threshold update algorithm based on its computational load, average offloading latency, average energy consumption, and edge server processing time, depending on the server utilization. We show that there always exists a unique Mean-Field Nash Equilibrium (MFNE) in the large-system limit when the task processing times of mobile devices follow an exponential distribution. This is achieved by carefully partitioning the space of mean arrival rates to account for the discrete structure of each device's optimal threshold. Moreover, we show that our proposed threshold update algorithm converges to the MFNE. Finally, we perform simulations to corroborate our theoretical results and demonstrate that our proposed algorithm still performs well in more general setups based on the collected real-world data and outperforms the well-known probabilistic offloading policy.