Predicting web service levels during VM live migrations

Abstract

The ability to live migrate virtual machines (VMs) between physical servers without any perceivable service interruption is pivotal for building more energy efficient Cloud Computing infrastructures in the future. Nevertheless, energy efficiency is not worth the effort if quality metrics (e.g., QoS, QoE) are severely decreased by, e.g., dynamic consolidation using live migration. We identify the most significant utilization metrics to predict the service level during live migrations for a web server scenario. We show important correlations, give reasons and draw conclusions for systems using live migration for yielding higher energy efficiency. We also give reasons for extending the current hypervisors' capabilities regarding VM utilization collection and reporting. We present the effects of live migration on service levels for different workload scenarios. In particular, we demonstrate that live migration should be done preventively. This anticipates disproportional high service level degradation due to live migration. We examine the most important utilization metrics for predicting the service level by both stepwise and exhaustive regression. As a result, we can explain 90% of the service level variance during live migration with a single variable, using more variables yields 95%.