Contextual Information Based Scheduling for Service Migration in Mobile Edge Computing

Abstract

Mobile Edge Computing (MEC) is a distributed computing paradigm that delivers processing and data storage capabilities closer to the network edge, which is adjacent to mobile consumers and devices. MEC lowers latency, reduces data transmission times, and improves overall performance for mobile apps by relocating computing resources to the network’s edge. But, due to higher average load and longer elapsed time, modern end devices such as smartphones and tablets cause major load challenges in mobile computing networks. Furthermore, if smartphones cause unpredictable traffic patterns, it becomes impossible to model and forecast the nature of communication. Such confusing traffic figures are caused not just by bursty Internet traffic, but also by multitasking operating systems that allow users to swiftly switch between active apps. Mobility of users and end devices impose a difficult challenge to provide continuous services in mobile computing. In this paper, this issue is addressed using the Contextual Information Based Scheduling (CIBS) technique to optimally allocate resources and provide seamless service to the users. The proposed method is implemented with NS-3, an open-source network simulator that provides a comprehensive set of modules for Mobile Edge Computing (MEC) simulations, including mobility modelling support. The experimental results show that CIBS offers migration time of 97512ms, delay time of 372115ms, execution time of 1061328ms and downtime of 98715ms. The results are compared with the existing Mobility-Aware Joint Task Scheduling (MATS) approach. The obtained results show that CIBS outperforms MATS with regard to migration time, latency, execution time and downtime.