Abstract
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.
Highlights
In the Industrial Internet, computing- and power-limited mobile devices (MDs) related to the production process can hardly support computation-intensive and time-sensitive applications, such as smart sensing for production environments, healthcare monitoring of production machines, and smart transportation of production materials [1,2,3,4]
We solve the problem with alternating optimization (AP) methods and, based on these, propose and design a heuristic algorithm, heuristic algorithm based on greedy policy (HAGP), to make decisions for processing computation tasks on MDs, which would minimize the system overhead consisting of the weighted sum of the process time delay and energy consumption
In the Mobile edge computing (MEC) system described in this paper, the computation tasks can be executed locally or transmitted to the edge server for processing, while both of them will consume the energy stored in MDs, which is needed to ensure the reliability of MDs
Summary
In the Industrial Internet, computing- and power-limited mobile devices (MDs) related to the production process can hardly support computation-intensive and time-sensitive applications, such as smart sensing for production environments, healthcare monitoring of production machines, and smart transportation of production materials [1,2,3,4]. Considering the importance of MDs for task offloading in MEC, an energy-efficient task offloading scheme was studied in [21], which satisfies the reliability existing in local and offloading schemes because of the uncertain computing power and transmission rate, respectively It is still questionable for the reliability of MDs measured by the battery level. To make the task offloading scheme more suitable for the actual production environment, the limited energy power is an important constraint that needs to be met To address this issue, this paper focuses on the reliability of MDs when making the offloading decision in an MEC system. We solve the problem with alternating optimization (AP) methods and, based on these, propose and design a heuristic algorithm, HAGP, to make decisions for processing computation tasks on MDs, which would minimize the system overhead consisting of the weighted sum of the process time delay and energy consumption.
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