Embedding individualized machine learning prediction models for energy efficient VM consolidation within Cloud data centers

Abstract

The fast growth in demand for utility-based IT services has lead to the formation of large scale Cloud data centers. The electrical energy consumption of these data centers results in high operational costs and carbon dioxide emissions. Cloud data centers benefit from the use of virtualization technology to reduce their energy consumption. This technology enables a Cloud data center to allocate its physical resources (CPU, memory, hard disk, network bandwidth) on demand and balance loads between their physical hosts by live migration of Virtual Machines (VMs). However, the migration of VMs can result in Service Level Agreement Violations (SLAVs) and consequently low Quality of Service (QoS). Hence, in this paper, we propose an energy aware VM consolidation algorithm that minimizes SLAVs. Dynamic VM consolidation has three stages: a) Detecting over- and under-utilized hosts; b) Selecting one or more VMs for migration from those hosts; c) Finding destination hosts for the selected VMs. Therefore, the proposed VM consolidation algorithm contains different models for each stage. For the first stage, we developed different fine-tuned Machine Learning (ML) prediction models for individual VMs to predict the best time to trigger migrations from hosts. For the second stage, we lexicographically consider migration time and host CPU usage when selecting VMs to migrate. Finally, a new method based on the Best Fit Decreasing (BFD) algorithm was developed to select a destination host for the VMs being migrated. Our algorithm was compared to a baseline VM consolidation algorithm that used Local Regression for detecting over-utilized hosts, minimum migration time for the VM selection stage and power-aware best fit for the host selection stage. The comparison demonstrated that our VM consolidation algorithm improved energy consumption and SLAVs by 26% and 50%, respectively.