Trace-driven Modeling and Verification of a Mobility-Aware Service Allocation and Migration Policy for Mobile Edge Computing

Abstract

In recent times, Mobile Edge Computing (MEC) has emerged as a new paradigm allowing low-latency access to services deployed on edge nodes offering computation, storage and communication facilities. Vendors deploy their services on MEC servers to improve performance and mitigate network latencies often encountered in accessing cloud services. An allocation policy determines how to allocate service requests from mobile users to MEC servers. A number of proposals for binding user service requests to nearby edge servers enroute have been proposed in literature. However, none of these proposals, to the best of our knowledge, provide quantitative performance guarantees on the quality of service metrics. Indeed, the evolving environment, along with a large allocation configuration space makes proving performance guarantees for such allocation policies a challenging task. To address such issues, we propose a trace driven approach to derive a formal model of allocation policies and perform quantitative verification to produce probabilistic guarantees on performance metrics. We use benchmark real world MEC server and user datasets and a mobility aware allocation and migration policy from recent literature to validate our model. Experimental results show our model's effectiveness in quantitatively reasoning about service allocation performance metrics in MEC systems.