Learning Automata for Multi-Access Edge Computing Server Allocation with Minimal Service Migration

Multi-access edge computing nodes are being developed in support of next generation applications, which require high capacity, high reliability and low latency. One important problem that the research community has recently focused on is the allocation strategy of applications to the different MEC server nodes. In our approach, rather than focusing on maximizing usage of resources, we focus on the minimization of migration events, which can create significant service downtime to applications that need low latency and high reliability, in addition to increasing traffic congestion in the underlying network. This paper introduces a priority induced service migration minimization (PrISMM) algorithm, which aims at minimizing service migration for both high and low priority services, through the use of learning automata. We carry out extensive simulations and produce results showing its effectiveness in reducing the mean service downtime of lower priority services and the mean admission time of the higher priority services.