Flexible computation offloading in a fuzzy-based mobile edge orchestrator for IoT applications

• Published in: Journal of cloud computing : advances, systems and applications Vol. 9; no. 1; p. 66
• Main Authors: Nguyen, VanDung, Khanh, Tran Trong, Nguyen, Tri D. T., Hong, Choong Seon, Huh, Eui-Nam
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 25.11.2020; Springer Nature B.V
• Subjects: Algorithms; Ambient intelligence; Augmented reality; Cloud computing; Computation offloading; Computer Communication Networks; Computer Science; Computer System Implementation; Computer Systems Organization and Communication Networks; Context; Control systems design; Digital media; Edge computing; Edge-cloud computing cooperation for task offloading in internet-of-things; Electronic devices; Information Systems Applications (incl.Internet); Internet of Things; Local area networks; Resource management; Software Engineering/Programming and Operating Systems; Special Purpose and Application-Based Systems; Video transmission; Wireless networks; Fuzzy logic; Mobile edge orchestrator; Internet of Things; Offload decision
• ISSN: 2192-113X; 2192-113X
• DOI: 10.1186/s13677-020-00211-9

In the Internet of Things (IoT) era, the capacity-limited Internet and uncontrollable service delays for various new applications, such as video streaming analysis and augmented reality, are challenges. Cloud computing systems, also known as a solution that offloads energy-consuming computation of IoT applications to a cloud server, cannot meet the delay-sensitive and context-aware service requirements. To address this issue, an edge computing system provides timely and context-aware services by bringing the computations and storage closer to the user. The dynamic flow of requests that can be efficiently processed is a significant challenge for edge and cloud computing systems. To improve the performance of IoT systems, the mobile edge orchestrator (MEO), which is an application placement controller, was designed by integrating end mobile devices with edge and cloud computing systems. In this paper, we propose a flexible computation offloading method in a fuzzy-based MEO for IoT applications in order to improve the efficiency in computational resource management. Considering the network, computation resources, and task requirements, a fuzzy-based MEO allows edge workload orchestration actions to decide whether to offload a mobile user to local edge, neighboring edge, or cloud servers. Additionally, increasing packet sizes will affect the failed-task ratio when the number of mobile devices increases. To reduce failed tasks because of transmission collisions and to improve service times for time-critical tasks, we define a new input crisp value, and a new output decision for a fuzzy-based MEO. Using the EdgeCloudSim simulator, we evaluate our proposal with four benchmark algorithms in augmented reality, healthcare, compute-intensive, and infotainment applications. Simulation results show that our proposal provides better results in terms of WLAN delay, service times, the number of failed tasks, and VM utilization.