DeepDive: transparently identifying and managing performance interference in virtualized environments

Abstract

Cloud computing in general, and Infrastructure-as-a-Service (IaaS) in particular, are becoming ever more popular. Unfortunately, performance interference (and the resulting unpredictability in the delivered performance) across virtual machines (VMs) co-located on the same physical machine (PM) threatens to make cloud computing inadequate for performance-sensitive customers and more expensive than necessary for all customers. We describe the design and implementation of DeepDive, a system for transparently identifying and managing interference. DeepDive successfully addresses several important challenges, including limiting overhead and requiring no performance information from applications. We first show that it is possible to use easily obtainable, low-level metrics to clearly discern when interference is occurring and what resource is causing it. Using realistic workloads, we demonstrate that DeepDive quickly learns about interference across co-located VMs. Moreover, we show DeepDive’s ability to deal efficiently with interference when it is detected, by using a low-overhead approach to selecting an alternative PM for a VM that is causing interference at its current PM.