Full metadata
Title
A Comparative Study on the Performance Isolation of Virtualization Technologies
Description
Virtualization technologies are widely used in modern computing systems to deliver shared resources to heterogeneous applications. Virtual Machines (VMs) are the basic building blocks for Infrastructure as a Service (IaaS), and containers are widely used to provide Platform as a Service (PaaS). Although it is generally believed that containers have less overhead than VMs, an important tradeoff which has not been thoroughly studied is the effectiveness of performance isolation, i.e., to what extent the virtualization technology prevents the applications from affecting each other’s performance when they share the resources using separate VMs or containers. Such isolation is critical to provide performance guarantees for applications consolidated using VMs or containers. This paper provides a comprehensive study on the performance isolation for three widely used virtualization technologies, full virtualization, para-virtualization, and operating system level virtualization, using Kernel-based Virtual Machine (KVM), Xen, and Docker containers as the representative implementations of these technologies. The results show that containers generally have less performance loss (up to 69% and 41% compared to KVM and Xen in network latency experiments, respectively) and better scalability (up to 83.3% and 64.6% faster compared to KVM and Xen when increasing number of VMs/containers to 64, respectively), but they also suffer from much worse isolation (up to 111.8% and 104.92% slowdown compared to KVM and Xen when adding disk stress test in TeraSort experiments under full usage (FU) scenario, respectively). The resource reservation tools help virtualization technologies achieve better performance (up to 85.9% better disk performance in TeraSort under FU scenario), but cannot help them avoid all impacts.
Date Created
2019
Contributors
- Huang, Zige (Author)
- Zhao, Ming (Thesis advisor)
- Sarwat, Mohamed (Committee member)
- Wang, Ruoyu (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
60 pages
Language
eng
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.I.55483
Level of coding
minimal
Note
Masters Thesis Computer Science 2019
System Created
- 2020-01-14 09:12:44
System Modified
- 2021-08-26 09:47:01
- 3 years 3 months ago
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