Abstract
Cloud computing makes large infrastructure capacities available to users in a flexible and affordable fashion, which is of specific interest to scientists for conducting experiments. Unfortunately, our past research has provided first indications that virtual machines – the most popular type of cloud-based infrastructure – have substantial deficits with respect to time measurements, which are an important tool for researchers. In this paper, we provide a detailed analysis on the accuracy of time measurements based on various machine configurations. They cover influence factors such as machine type, virtualization solution, and programming language. The results indicate that not the use of virtualization as such, but the potentially uncontrollable utilization of the physical host is a decisive factor for the accuracy of time measurements. Different virtualization solutions and programming languages play an inferior role. Our findings, along with the publicly released tool TiMeAcE.KOM, can provide a valuable decision support for researchers in the selection and configuration of cloud-based experimental infrastructures.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Owens, D.: Securing Elasticity in the Cloud. Comm. of the ACM 53(6), 46–51 (2010)
Briscoe, G., Marinos, A.: Digital Ecosystems in the Clouds: Towards Community Cloud Computing. In: Proc. of DEST 2009 (2009)
Lampe, U., Miede, A., Richerzhagen, N., Schuller, D., Steinmetz, R.: The Virtual Margin of Error – On the Limits of Virtual Machines in Scientific Research. In: Proc. of CLOSER 2012 (2012)
Silver, E.: An Overview of Heuristic Solution Methods. J. of the Operational Research Society 55, 936–956 (2004)
Lampe, U., Kieselmann, M., Miede, A., Zöller, S., Steinmetz, R.: On the Accuracy of Time Measurements in Virtual Machines. In: Proc. of CLOUD 2013 (2013)
Jain, R.K.: The Art of Computer Systems Performance Analysis: Techniques for Experimental Design, Measurement, Simulation, and Modeling. Wiley (1991)
VMware, Inc.: Timekeeping in VMware Virtual Machines (2011), http://www.vmware.com/files/pdf/techpaper/Timekeeping-In-VirtualMachines.pdf
Chen, H., Jin, H., Hu, K.: XenHVMAcct: Accurate CPU Time Accounting for Hardware-Assisted Virtual Machine. In: Proc. of PDCAT 2010 (2010)
Broomhead, T., Cremean, L., Ridoux, J., Veitch, D.: Virtualize Everything But Time. In: Proc. of OSDI 2010 (2010)
El-Khamra, Y., Kim, H., Jha, S., Parashar, M.: Exploring the Performance Fluctuations of HPC Workloads on Clouds. In: Proc. of CloudCom 2010 (2010)
Schad, J., Dittrich, J., Quiané-Ruiz, J.: Runtime Measurements in the Cloud: Observing, Analyzing, and Reducing Variance. In: Proc. of the VLDB Endowment, vol. 3(1–2), pp. 460–471 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lampe, U., Kieselmann, M., Miede, A., Zöller, S., Steinmetz, R. (2013). A Tale of Millis and Nanos: Time Measurements in Virtual and Physical Machines. In: Lau, KK., Lamersdorf, W., Pimentel, E. (eds) Service-Oriented and Cloud Computing. ESOCC 2013. Lecture Notes in Computer Science, vol 8135. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40651-5_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-40651-5_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40650-8
Online ISBN: 978-3-642-40651-5
eBook Packages: Computer ScienceComputer Science (R0)