An efficient and optimized Markov chain-based prediction for server consolidation in cloud environment

Abstract

Cloud service providers are aggressively expanding their data centers to meet end-user demand, resulting in increased power and energy consumption. As a result, it becomes extremely important to implement server consolidation solutions in the context of cloud computing. One of the difficult problems in cloud data centers is efficient server consolidation, which is related to both environmental and financial aspects. Using resources efficiently and reducing energy consumption are the main goals of consolidating servers for cloud computing. Regrettably, the majority of methods now in use have been geared towards either lowering system performance or reducing energy consumption. Using the Markov chain principle for each server transition, we suggested a strategy in this study to optimize the server consolidation process. An early review of the study’s findings is encouraging and comes to the conclusion that a successful server consolidation strategy may be implemented with the least amount of resources and the best possible energy use. The suggested strategy takes into account the following three elements: consolidating the resources, cutting down energy consumption, and trading off of both (composite case).