Confidence interval‐based overload avoidance algorithm for virtual machine placement. (29th July 2022) - Ahmadi, Javad; Toroghi Haghighat, Abolfazl; Rahmani, Amir Masoud; Ravanmehr, Reza
Abstract: Virtualization plays an essential role in decreasing energy consumption and optimizing resource utilization by enabling the creation of virtual machines (VM) and their consolidation through live migration. Excessive migrations and a lack of required VMs are two critical factors in QoS degradation. The current consolidation approaches impose an intensive time complexity and cannot be used in large data centers with hundreds of hosts. This article proposes a framework for dynamic consolidation divided into a QoS‐aware algorithm for overload avoidance and a power‐aware algorithm for VM placement. To compute a safe zone criterion for any VM, relations were suggested by applying an interval estimate with a confidence level. By employing this criterion, the offered algorithm could guarantee the quality of service (QoS), particularly for specific VMs, while avoiding overhead. The VM placement algorithm is developed based on the maximum utilization of active hosts. It provides the capability to control the number of active hosts for the data center manager. The simulation results with real workloads revealed that the proposed framework could decline the amount of service level agreement violations by 78% and the number of migrations by 74%, and energy consumption by up to 13% in comparison with the best results of the benchmark algorithms. Hence, the application of this framework upgrades the QoS of data centers and declines their energy costs. Abstract : A novel concept called a safe zone was introduced and new algorithm for overhead avoidance based on the safe zone and applied it to two different conditions: the overload occurrence in the case of no VM reductions placed on a host; and overload by admitting a new VM into the host. An energy‐aware algorithm was proposed for VM placement by decreasing data center energy consumption and low complexity. We presented the required framework for using both algorithms.