Cloud computing is an emerging technology through which resources can be shared over the internet with different users either free or on a rent basis. Resource scheduling in cloud computing is a challenging area for researchers as is maximum utilization can opt through efficient resource scheduling algorithm. Other than this, virtual machine provisioning, packaging, and availability guarantee decrease the performance. Resource management in cloud could be a time and cost effective activity if it is managed property. These resources are accessible and computable which is totally dependent upon the management techniques applied in cloud.In a cloud setting, heterogeneous, vulnerability, and scattering of resources creates many issues of distribution among the workloads which need to be compute. Specialists still face inconveniences to pick the prudent, material and expend less time to execution of resource portion to the cloud. This investigation delineates an expansive composed writing examination of asset administration inside the space of cloud typically and cloud asset administration based on SLA with multi-objective functions like cost and time. In this paper, an autonomic cloud resource management technique is proposed to resolve identified issues by adopting the self-characteristics mechanism and improved Antlion optimization algorithm and tested in cloudsim toolkit and Aws Ec2 environment. The implementation results of proposed work are the evidence that it is better performing as compared with the existing frameworks, however, the performance evaluation method depends upon the different cloud environment and it may vary.
Cellular Particle Swarm Scheduling Algorithm for Virtual Resource Scheduling of Cloud Computing
