VM consolidation improvement approach using heuristics granular rules in cloud computing environment

Abstract

Since cloud computing has been developing as a successful business and growing interest of organizations to make use of the environment, the volume of provided services is highly demanding. Cloud computing environment has high maintenance costs, therefore, the resources are limited; due to rapid progress and the highly demanded volume of utilization of resources, ascertaining an appropriate approach for controlling and managing resources has become essential in this area. Therefore, an algorithm must be designed to perform better in all QoS. A significant factor in current exploratory methods is that each method owns distinctive features and perhaps does not estimate all QoSes. For example, one specific method has low energy consumption but does not consider SLA violation. Concerning the aims of the present study, the granular model is designed which uses various criteria involved in computations; in practice, by entering the values given in the past and discovering the relationships among them, the most desirable classification is produced. The membership function and the inference rule are derived from the data provided by the actual workload. Experimental analysis of the results showed that this method works out better than all other related methods and avails consistent performance in all QoS criteria.