Self-adaptive architecture for virtual machines consolidation based on probabilistic model evaluation of data centers in Cloud computing

By employing the virtual machines (VMs) consolidation technique at a virtualized data center, optimal mapping of VMs to physical machines (PMs) can be performed. The type of optimization approach and the policy of detecting the appropriate time to implement the consolidation process are influential in the performance of the consolidation technique. In a majority of researches, the consolidation approach merely focuses on the management of underloaded or overloaded PMs, while a number of VMs could also be in an underload or overload state. Managing an abnormal state of VM results in the postponement of PM getting into an abnormal state as well and affects the implementation time of the consolidation process. For the aim of optimal VM consolidation in this research, a self-adaptive architecture is presented to detect and manage underloaded and overloaded VMs/PMs in reaction to workload changes in the data center. The goal of consolidation process is employing the minimum number of active VMs and PMs, while guaranteeing the quality of service (QoS). Assessment criteria of QoS are two parameters including average number of requests in the PM buffer and average waiting time in the VM. To evaluate these two parameters, a probabilistic model of the data center is proposed by applying the queuing theory. The assessment results of the probabilistic model form a basis for decision-making in the modules of the proposed architecture. Numerical results obtained from the assessment of the probabilistic model via discrete-event simulator under various parameter settings confirm the efficiency of the proposed architecture in achieving the aims of the consolidation process.