Resource Caching and Task Migration Strategy of Small Cellular Networks under Mobile Edge Computing

Abstract

As computing-intensive mobile applications become increasingly diversified, mobile devices’ computing power is hard to keep up with demand. Mobile devices migrate tasks to the Mobile Edge Computing (MEC) platform and improve the performance of task processing through reasonable allocation and caching of resources on the platform. Small cellular networks (SCN) have excellent short-distance communication capabilities, and the combination of MEC and SCN is a promising research direction. This paper focuses on minimizing energy consumption for task migration in small cellular networks and proposes a task migration energy optimization strategy with resource caching by combining optimal stopping theory with migration decision-making. Firstly, the process of device finding the MEC platform with the required task processing resources is formulated as the optimal stopping problem. Secondly, we prove an optimal stopping rule’s existence, obtain the optimal processing energy consumption threshold, and compare it with the device energy consumption. Finally, the platform with the best energy consumption is selected to process the task. In the simulation experiment, the optimization strategy has lower average migration energy consumption and higher average data execution energy efficiency and average distance execution energy efficiency, which improves task migration performance by 10% ∼ 60%.

Energy‐Efficient Task Migration and Path Planning in UAV‐Enabled Mobile Edge Computing System

“…ere are some research efforts on dynamic task migration [5][6][7][8][9]. Wang et al [5] described the task migration problem as a Markov decision process and designed an optimal service migration strategy in MEC.…”

“…ey proposed an iterative algorithm to reduce energy consumption and achieve optimal delay performance. Moreover, the authors of [7] proposed a task migration energy optimization strategy by considering resource caching. Meanwhile, Anajemba et al [8] studied cooperative offloading scheme in energy-efficient multiaccess edge computing and proposed a Lagrangian suboptimal convergent computation offloading algorithm.…”

“…With an optimal task migration path, UAVs can pay less migration energy cost under load balancing. In the literature, there have been some studies focused on task migration [5][6][7][8][9] in MEC and optimization UAV-assisted network [10][11][12][13][14]. However, on the one hand, these research works mainly focus on energy consumption optimization, while neglecting considering load balance in MEC system.…”