@article{MAKHILLAJIT202019126819,
    title = {Improved Particle Swarm Optimization for Virtual Machine Selection in Cloud Datacenter},
    journal = {Asian Journal of Information Technology},
    volume = {19},
    number = {12},
    pages = {284-288},
    year = {2020},
    issn = {1682-3915},
    doi = {ajit.2020.284.288},
    url = {https://makhillpublications.co/view-article.php?issn=1682-3915&doi=ajit.2020.284.288},
    author = {R.B.,Harshvardhan and},
    keywords = {particle swarm optimization,virtual machine optimization,Cloud computing,energy efficiency,resource allocation,nature inspired algorithms},
    abstract = {To meet the ever-growing demand for the
online computational resources, it is mandatory to have
the best resource allocation algorithm to allocate the
resources to its end users. Virtual machine placement is
the key technology in improving the resource utilization
and thereby reduces the power consumption. In this
study, particle swarm optimization algorithm is used to
address VM-PM placement problem. This can be
addressed by reducing the number of physical machines
over the cloud datacenters. In our study, we discuss how
to improve the efficiency of particle swarm Intelligence
by adapting efficient mechanism to reduce the power
consumption in cloud data centers by maximizing the
resource utilization. The obtained results shows that
proposed Particle Swarm Optimization (PSO) provide
the optimized solution compared to the existing
algorithms.}
    }