Profiling and Understanding Virtualization Overhead in Cloud

• Published in: 2015 44th International Conference on Parallel Processing pp. 31 - 40
• Main Authors: Liuhua Chen, Patel, Shilkumar, Haiying Shen, Zhongyi Zhou
• Format: Conference Proceeding Journal Article
• Language: English
• Published: IEEE 01.09.2015
• Subjects: Accuracy; Bandwidth; Cloud computing; Construction; Data centers; Mathematical models; Performance evaluation; Provisioning; Resource allocation; Resource management; Resource provisioning; Resources utilization; Virtual machine monitors; Virtualization; Virtualization overhead; Workload; Yttrium
• ISSN: 0190-3918; 2332-5690
• DOI: 10.1109/ICPP.2015.12

Virtualization is a key technology for cloud data centers to implement infrastructure as a service (IaaS) and to provide flexible and cost-effective resource sharing. It introduces an additional layer of abstraction that produces resource utilization overhead. Disregarding this overhead may cause serious reduction of the monitoring accuracy of the cloud providers and may cause degradation of the VM performance. However, there is no previous work that comprehensively investigates the virtualization overhead. In this paper, we comprehensively measure and study the relationship between the resource utilizations of virtual machines (VMs) and the resource utilizations of the device driver domain, hypervisor and the physical machine (PM) with diverse workloads and scenarios in the Xen virtualization environment. We examine data from the real-world virtualized deployment to characterize VM workloads and assess their impact on the resource utilizations in the system. We show that the impact of virtualization overhead depends on the workloads, and that virtualization overhead is an important factor to consider in cloud resource provisioning. Based on the measurements, we build a regression model to estimate the resource utilization overhead of the PM resulting from providing virtualized resource to the VMs and from managing multiple VMs. Finally, our trace-driven real-world experimental results show the high accuracy of our model in predicting PM resource consumptions in the cloud datacenter, and the importance of considering the virtualization overhead in cloud resource provisioning.