Measuring Effective Scheduling Interval Against VM Load with Rapid Elasticity in Cloud Computing

Abstract

Innovative and useful cloud based applications are increasing regularly as its demand also growing. The computing load, either processing or data load, on such application has been increased in multiples during the covid-19 pandemic, because everyone likes to do its jobs through online solutions either for meeting, teaching-learning, presentation, discussion, social data sharing, etc. as these are safe and secure solutions for all. This all increases the computing load proportionally the requirement of resources on the cloud server or Host machine. The host machine is the physical machine that contains the computing resources like storage, processors, bandwidth etc. for cloud based applications. These computing resources are shared and managed between multiple virtual machines in such an efficient manner that if computing load increases very much on some applications, while at the same time on the same server other application's resource utilization in underuse, then these resources will be shared to the applications currently having overloaded with the processing load. As cloud computing provides a pay-per-use model and hence if the resources utilization is managed efficiently and effectively the processing cost incurred to the client or provider will be more cost effective. So, in this paper we will measure the effectiveness of scheduling interval against increasing VM load on rapid elasticity for the existing cloud infrastructure available on the Host Machine.