Modeling and Verification of Service Allocation Policies for Multi-Access Edge Computing Using Probabilistic Model Checking

Abstract

In recent times, Multi-Access 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 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 guarantees on performance metrics. Indeed, the evolving environment, along with a large allocation configuration space makes proving performance guarantees for such allocation policies a challenging task. Further, the implications of MEC server failures on allocation policies have been relatively unexplored. 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 the San Francisco taxi dataset, the LDNS availability dataset and allocation policies from recent literature to validate our approach. Experimental results demonstrate how our model can be utilized to quantitatively compare performance metrics of service allocation policies in MEC systems.