Artificial neural network-based virtual machine allocation in cloud computing

Abstract

The expeditious development in information technology visualizes that facilities for computing are one of the prime utilities of life after the other main essential utilities i.e., water, gas, and electricity. With the help of virtualization, cloud computing helps in transparently sharing of data and services between the cloud users and provides access to more than thousands of computers as a single instance. The virtual machine allocation is a challenging task in virtualization which is regulated as a crucial part of the virtual machine (VM) migration. It is done to discover the best assignment of VMs on physical machines (PMs) because it has explicit effects on resource utilization, energy efficiency, the performance of running applications, etc. So, there is a need to design an effective method for the VM placement problem. The research paper presents a virtual machine allocation technique implementing Enhanced-Modified Best Fit Decreasing (E-MBFD) Algorithm. The outcome of the proposed technique is the cross-validation of allocated virtual machines on physical machines using Artificial Neural Network (ANN). Also, the proposed technique is implemented with the merits of finding the false allocations which happen due to inefficient utilization of resources and it helps in re-allocation of these VMs. The empirical result shows that the E-MBFD technique outperforms in terms of minimized power consumption and a lesser number of service-level agreement (SLA) violations in contrast with the conventional technique.