MultiOff: offloading support and service deployment for multiple IoT applications in mobile edge computing

Mobile Edge Computing (MEC) provides a new opportunity to reduce the latency of IoT applications significantly. It does so by offloading computation-intensive tasks in applications from IoT devices to mobile edges, which are located N-close proximity to the IoT devices. However, the prior researches focus on supporting computation offloading for a specific type of applications. Meanwhile, making multi-task and multi-server offloading decisions in highly complex and dynamic MEC environments remains intractable. To address this problem, this paper proposes a novel approach called MultiOff. First, we propose a generic program structure that supports on-demand computation offloading. Applications conforming to this structure can extract the flowcharts of program fragments via code analysis. Second, a novel cost-efficient offloading strategy based on a Multi-task Particle Swarm Optimization algorithm using the Genetic Algorithm operators (MPSO-GA) is proposed. MPSO-GA makes offloading decisions by analyzing program fragment flowcharts and context. Finally, each application can be offloaded at the granularity of services with the offloading scheme, minimizing the system cost while satisfying the deadline constraint for each application. We evaluate MultiOff on several real-world applications and the experimental results show that MultiOff can support computation offloading for different types of applications at the fine-grained granularity of services. Moreover, MPSO-GA can save about 2.11–17.51%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document} system cost compared with other classical methods while meeting time constraints.