Scalable correlation-aware virtual machine consolidation using two-phase clustering

• Main Authors: Xi Li, Ventresque, Anthony, Iglesias, Jesus Omana, Murphy, John
• Format: Conference Proceeding
• Language: English
• Subjects: Clustering; Clustering algorithms; Consolidation; Correlation; Data centers; Interference; Mathematical models; Performance degradation; Placement; Resource management; Scalability; Servers; Time series analysis; Virtual environments; VM placement
• DOI: 10.1109/HPCSim.2015.7237045

Server consolidation is the most common and effective method to save energy and increase resource utilization in data centers, and virtual machine (VM) placement is the usual way of achieving server consolidation. VM placement is however challenging given the scale of IT infrastructures nowadays and the risk of resource contention among co-located VMs after consolidation. Therefore, the correlation among VMs to be co-located need to be considered. However, existing solutions do not address the scalability issue that arises once the number of VMs increases to an order of magnitude that makes it unrealistic to calculate the correlation between each pair of VMs. In this paper, we propose a correlation-aware VM consolidation solution ScalCCon 1 , which uses a novel two-phase clustering scheme to address the aforementioned scalability problem. We propose and demonstrate the benefits of using the two-phase clustering scheme in comparison to solutions using one-phase clustering (up to 84% reduction of execution time when 17, 446 VMs are considered). Moreover, our solution manages to reduce the number of physical machines (PMs) required, as well as the number of performance violations, compared to existing correlation-based approaches.