Performance Evaluation of Neural Networks-Based Virtual Machine Placement Algorithm for Server Consolidation in Cloud Data Centres

Cloud computing, an on-demand model that provides IT services as a utility, has become increasingly popular in recent years. To the contrary, the cloud's resources are housed in data centres that have a major impact on the environment due to their high energy consumption. As a result, consolidating servers into fewer physical locations is a significant method for increasing data centre efficiency and reducing energy waste. In this work, we propose a technique for selecting and deploying servers and virtual machines using artificial neural networks. It forecasts user demand, evenly distributes work if the server is overloaded, trains a feed forward neural network with back propagation learning, uses a selection algorithm to determine which virtual machine to use, and finally uses a cross-validation algorithm to ensure that the placement was accurate. Cloud computing providers like Amazon (EC2), Microsoft Azure, and Google Cloud Storage provide the data used to measure the efficacy of server consolidation efforts. The algorithm's experimental results are compared to those of other algorithms published by the same author, both in exact and heuristic optimization methods, that aim to achieve the same goals.