Virtual Machine Migration in Cloud Computing using Artificial Intelligence

Abstract

Cloud Computing is known as the most fast evolving computing platform which is the future of supercomputing. A time will come when everyone would be on the cloud network and at that time, it would be essential for the cloud network to perform well. Cloud is also a computing server and hence, it takes every order as million instruction set. These instruction sets are often referred as Jobs. Scheduling an instruction set or job requires a lot of computing and one wrong placement may lead to wastage of energy units. The proposed work has taken these matters in a very serious manner and has designed an architecture diagram which deal with the job scheduling process from start to end. The proposed algorithm covers placement of the job at server, monitoring of the server to prevent them from overloading and when they are exhausted from jobs, the creation of Virtual Machine. The presented algorithm improves the Modified Best Fit Decreasing Algorithm by introducing artificial intelligence to it. Job handling has been done by using one of the finest swarm intelligence techniques known as Cuckoo search Algorithm that monitors the performance of the servers or host in order to check that they do not get overloaded. The proposed architecture has been evaluated on the basis of energy consumption, Service Level Agreement violation and total number of migrations.