School of Computer Science & School of Software Engineering, Sichuan University, Chengdu 610065, China.
School of Mathematics and Computer Science, Northwest Minzu University, Lanzhou 730050, China.
Sensors (Basel). 2021 May 18;21(10):3513. doi: 10.3390/s21103513.
In the Industrial Internet, computing- and power-limited mobile devices (MDs) in the production process can hardly support the computation-intensive or time-sensitive applications. As a new computing paradigm, mobile edge computing (MEC) can almost meet the requirements of latency and calculation by handling tasks approximately close to MDs. However, the limited battery capacity of MDs causes unreliable task offloading in MEC, which will increase the system overhead and reduce the economic efficiency of manufacturing in actual production. To make the offloading scheme adaptive to that uncertain mobile environment, this paper considers the reliability of MDs, which is defined as residual energy after completing a computation task. In more detail, we first investigate the task offloading in MEC and also consider reliability as an important criterion. To optimize the system overhead caused by task offloading, we then construct the mathematical models for two different computing modes, namely, local computing and remote computing, and formulate task offloading as a mixed integer non-linear programming (MINLP) problem. To effectively solve the optimization problem, we further propose a heuristic algorithm based on greedy policy (HAGP). The algorithm achieves the optimal CPU cycle frequency for local computing and the optimal transmission power for remote computing by alternating optimization (AP) methods. It then makes the optimal offloading decision for each MD with a minimal system overhead in both of these two modes by the greedy policy under the limited wireless channels constraint. Finally, multiple experiments are simulated to verify the advantages of HAGP, and the results strongly confirm that the considered task offloading reliability of MDs can reduce the system overhead and further save energy consumption to prolong the life of the battery and support more computation tasks.
在工业互联网中，生产过程中计算能力和电力有限的移动设备 (MD) 很难支持计算密集型或时间敏感型应用。作为一种新的计算范例，移动边缘计算 (MEC) 可以通过处理接近 MD 的任务来满足延迟和计算要求。然而，MD 的有限电池容量导致 MEC 中的任务卸载不可靠，这将增加系统开销并降低实际生产中的制造经济效益。为了使卸载方案适应这种不确定的移动环境，本文考虑了 MD 的可靠性，将其定义为完成计算任务后的剩余能量。更详细地说，我们首先研究了 MEC 中的任务卸载，并且还将可靠性作为一个重要标准。为了优化由任务卸载引起的系统开销，我们构建了两种不同计算模式（本地计算和远程计算）的数学模型，并将任务卸载表述为混合整数非线性规划 (MINLP) 问题。为了有效地解决优化问题，我们进一步提出了一种基于贪婪策略的启发式算法 (HAGP)。该算法通过交替优化 (AP) 方法为本地计算确定最佳 CPU 周期频率，并为远程计算确定最佳传输功率。然后，它通过贪婪策略在这两种模式下为每个 MD 做出最优的卸载决策，以最小化系统开销。最后，通过多个实验来验证 HAGP 的优势，结果强烈证实了所考虑的 MD 任务卸载可靠性可以降低系统开销，进一步节省能源消耗，延长电池寿命，并支持更多的计算任务。
