Abstract

Virtualization is an essential technology in modern datacenters. Despite advantages such as security isolation, fault isolation, and environment isolation, current virtualization techniques do not provide effective performance isolation between virtual machines (VMs). Specifically, hidden contention for physical resources impacts performance differently in different workload configurations, causing significant variance in observed system throughput. To this end, characterizing workloads that generate performance interference is important in order to maximize overall utility.
In this paper, we study the effects of performance interference by looking at system-level workload characteristics. In a physical host, we allocate two VMs, each of which runs a sample application chosen from a wide range of benchmark and real-world workloads. For each combination, we collect performance metrics and runtime characteristics using an instrumented Xen hypervisor. Through subsequent analysis of collected data, we identify clusters of applications that generate certain types of performance interference. Furthermore, we develop mathematical models to predict the performance of a new application from its workload characteristics. Our evaluation shows our techniques were able to predict performance with average error of approximately 5%.

Authors

• Georgia Institute of Technology: Younggyun Koh, Calton Pu
• EECS Department, Case Western Reserve University: Zhihua Wen
• Intel Corporation: Rob C. Knauerhase, Paul Brett, Mic Bowman

Conference

2007 IEEE International Symposium on Performance Analysis of Systems and Software http://ispass.org/ispass2007 PDF