Hypervisors’ Guest Isolation Capacity Evaluation in the Private Cloud Using SIAGR Framework

Full Text (PDF, 294KB), PP.57-63

Views: 0 Downloads: 0


P. Vijaya Vardhan Reddy 1,* Lakshmi Rajamani 1

1. Department of CSE, OU, Hyderabad, 500007, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijitcs.2015.04.06

Received: 20 Jun. 2014 / Revised: 4 Oct. 2014 / Accepted: 23 Dec. 2014 / Published: 8 Mar. 2015

Index Terms

Hypervisor, CloudStack, Virtualization, Para Virtualization, Full Virtualization, Hybrid Virtualization


Hypervisor vendors do claim that they have negated virtualization overhead compared to a native system. They also state that complete guest isolation is achieved while running multiple guest operating systems (OSs) on their hypervisors. But in a virtualization environment which is a combination of hardware, hypervisor and virtual machines (VMs) with guest operating systems, there bound to be an impact on each guest operating system while other guest operating systems are fully utilizing their allotted system resources. It is interesting to study hypervisor’s guest isolation capacity while several guest operating systems running on it. This paper selected three hypervisors, namely ESXi 4.1, XenServer 6.0 and KVM (Ubuntu 12.04 Server) for the experimentation. The three hypervisors are prudently preferred as they represent three different categories (full virtualized, para-virtualized, and hybrid virtualized). Focus being on hypervisors’ guest isolation capacity evaluation, therefore, private cloud is chosen over public cloud as it has fewer security concerns. Private Cloud is created using apache’s CloudStack. Windows 7 OS is deployed as a guest VM on each hypervisor and their guest isolation capacity is evaluated for CPU and Network performances.

Cite This Paper

P. Vijaya Vardhan Reddy, Lakshmi Rajamani, "Hypervisors’ Guest Isolation Capacity Evaluation in the Private Cloud Using SIAGR Framework", International Journal of Information Technology and Computer Science(IJITCS), vol.7, no.4, pp.57-63, 2015. DOI:10.5815/ijitcs.2015.04.06


[1]Nanda, S., T. Chiueh, ― A Survey on Virtualization Technologies, Technical report, Department of Computer Science, SUNY at Stony Brook, New York, 11794-4400, 2005. 

[2]VMware (2007) Understanding Full Virtualization, Para virtualization, and Hardware Assist. VMware, white paper Nov 10, 2007.

[3]Mell, P. & Grance, T. (2009) The NIST Definition of Cloud Computing. Version 15, 10-7-09. National Institute of Standards and Technology, Information Technology Laboratory.

[4]Buyya, R., Yeo, C.S., Venugopal, S., Broberg, J., Brandic, I. (2009) “Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility”. In: Future Generation Computer Systems, Elsevier B. V.

[5]CloudStack – OpenSource Cloud Computing platform Apache Organization [Online] http://cloudstack.apache.org

[6]Hyperic's System Information Gatherer (SIGAR) [Online] https://support.hyperic.com/display/SIGAR/Home

[7]VMware (2007) A Performance Comparison of Hypervisors VMware. White paper Feb 1, 2007.

[8]XenSource (2007) A Performance Comparison of Commercial Hypervisors. XenEnterprise vs. ESX Benchmark Results. 2007 XenSource.

[9]Standard Performance Evaluation Corporation (SPEC). [Online] http://www.spec.org/

[10]Passmark [Online] http://www.passmark.com

[11]FUJITSU, Benchmark Overview-vServCon, white paper, March 2010.

[12]Apparao, P. & Makineni, S. & Newell, D.Virtualization (2006) Characterization of network processing overheads in Xen. Technology in Distributed Computing, 2006. VTDC 2006

[13]Menon, A. et Al. (2005) Diagnosing Performance Overheads in the Xen Virtual Machine Environment. Conference on Virtual Execution Environments (VEE'05).

[14]Jianhua, C. & Qinming, H. & Qinghua, G. & Dawei, H. (2008) Performance Measuring and Comparing of Virtual Machine Monitors. Embedded and Ubiquitous Computing, 2008. EUC '08.

[15]Shan, Z. & Qinfen, H. (2009) Network I/O Path Analysis in the Kernel-based Virtual Machine Environment through Tracing. Information Science and Engineering (ICISE).

[16]Moller, K.T., ―Virtual Machine Benchmarking, Diploma Thesis, Karlsruhe Institute of Technology, 2007.

[17]P. V.V.Reddy, L. Rajamani, "Performance Comparison of Hypervisors in the Private Cloud", Advanced Computing, Networking and Informatics,vol.2,2014.DOI:10.1007/978-3-319-07350-7_44 http://link.springer.com/chapter/10.1007%2F978-3-319-07350-7_44

[18][Online] Netperf. http://www.netperf.org/netperf/

[19]P. V.V.Reddy and L. Rajamani "Evaluation of Different Hypervisors Performance in the Private Cloud with SIGAR Framework", International Journal of Advanced Computer Science and Applications, Vol.5, No.2, 2014. http://thesai.org/Downloads/Volume5No2/Paper_10-Evaluation_of_Different_Hypervisors_Performance_in_the_Private_Cloud_with_SIGAR_Framework.pdf

[20]P. V.V.Reddy, L. Rajamani ‘Performance Evaluation of Hypervisors in the Private Cloud based on System Information using SIGAR Framework and for System Workloads using Passmark’, International Journal of Advanced Science and Technology, Vol 70 (2014), pp.17-32. http://www.sersc.org/journals/IJAST/vol70/3.pdf.