Confidence interval-based overload avoidance algorithm for virtual machine placement

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.

Automated summary

This article proposes a dynamic consolidation framework to optimize energy consumption and resource utilization in data centers. By using QoS-aware and power-aware algorithms, the framework ensures load balancing of virtual machines and maximizes the use of active hosts. It guarantees QoS and avoids overhead for specific virtual machines. Simulation results with real workloads demonstrate that the proposed framework significantly improves service level agreement violations, migration numbers, and energy consumption.