Latency Minimization in a Fuzzy-Based Mobile Edge Orchestrator for IoT Applications

• Published in: IEEE communications letters Vol. 25; no. 1; pp. 84 - 88
• Main Authors: Nguyen, VanDung, Khanh, Tran Trong, Oo, Thant Zin, Tran, Nguyen H., Huh, Eui-Nam, Hong, Choong Seon
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Augmented reality; Cloud computing; Decision making; Delays; Edge computing; Electronic devices; Fuzzy logic; fuzzy-based approach; Internet of Things; latency minimization; Mobile edge orchestrator; Mobile handsets; Multiple criterion; Optimization; Servers; Task analysis; Wide area networks
• ISSN: 1089-7798; 1558-2558
• DOI: 10.1109/LCOMM.2020.3024957

Currently, matching the incoming Internet of Things applications to the current state of computing and networking resources of a mobile edge orchestrator (MEO) is critical for providing the high quality of service while temporally and spatially changing the incoming workload. However, MEO needs to scale its capacity concerning a large number of devices to avoid task failure and to reduce service time. To cope with this issue, we propose MEO with fuzzy-based logic that splits tasks from mobile devices and maps them onto the cloud and edge servers to reduce the latency of handling these tasks and task failures. A fuzzy-based MEO handles the multi-criteria decision-making process to decide where the offloaded task should run by considering multiple parameters in the same framework. Our approach selects the appropriate host for task execution and finds the optimal task-splitting strategy. Compared to the existing approaches, the service time using our proposal can achieve up to 7.6%, 22.6%, 38.9%, and 51.8% performance gains for augmented reality, healthcare, compute-intensive, and infotainment applications, respectively.