Reliability-Enhanced Task Offloading in Mobile Edge Computing Environments

• Published in: IEEE internet of things journal Vol. 9; no. 13; pp. 10382 - 10396
• Main Authors: Liu, Jialei, Zhou, Ao, Liu, Chunhong, Zhang, Tongguang, Qi, Lianyong, Wang, Shangguang, Buyya, Rajkumar
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.07.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Bandwidth; Bandwidth consumption; Bandwidths; Cloud computing; Collaboration; Completion time; Computation offloading; Consumption; Containers; Edge computing; Face recognition; Failure rates; Internet of Things; Internet of Things (IoT) application; Mobile computing; mobile edge computing; Multiple objective analysis; Network latency; Optimization; Reliability; reliability level; Task analysis; task offloading; Virtual environments; Virtual machining
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2021.3115807

Internet of Things (IoT) devices have become an integral part of our lives and are increasingly used in almost every field. Subsequently, there are a large number of latency-sensitive IoT applications (e.g., face recognition and autonomous driving) targeted for mobile edge computing environments. These IoT applications are often split into multiple collaborative tasks and offloaded onto containers or virtual machines (VMs) with certain failure rates and recovery rates. If these containers or VMs are not deployed in the same edge servers, the bandwidth resources of edge clouds must be consumed to transfer data. These factors increase the completion time of IoT applications to different degrees, and then affect their reliability level. Therefore, there exists equilibrium between the reliability level and bandwidth consumption. In this article, we investigate the equilibrium of minimizing the bandwidth consumption of IoT applications while maximizing the reliability level of these IoT applications during task offloading. We propose a multiobjective optimization problem, and transform it to a single-objective optimization problem. Furthermore, we introduce two efficient approaches to acquire two near-optimal solutions. The results of simulation experiments demonstrate that our proposed approaches can observably enhance the reliability level and reduce the bandwidth consumption of IoT applications compared with other related approaches. Meanwhile, we also make a comparative analysis of our proposed approaches.