Probabilistic performance modeling of virtualized resource allocation

  • Authors:

  • Brian J. Watson;Manish Marwah;Daniel Gmach;Yuan Chen;Martin Arlitt;Zhikui Wang

  • Affiliations:

  • HP Labs, Palo Alto, CA, USA;HP Labs, Palo Alto, CA, USA;HP Labs, Palo Alto, CA, USA;HP Labs, Palo Alto, CA, USA;Hp Labs, Palo Alto, CA, USA;HP Labs, Palo Alto, CA, USA

  • Venue:
  • Proceedings of the 7th international conference on Autonomic computing
  • Year:
  • 2010

Quantified Score

Hi-index 0.00

Visualization

Abstract

Virtualization technologies enable organizations to dynamically flex their IT resources based on workload fluctuations and changing business needs. However, only through a formal understanding of the relationship between application performance and virtualized resource allocation can over-provisioning or over-loading of physical IT resources be avoided. In this paper, we examine the probabilistic relationships between virtualized CPU allocation, CPU contention, and application response time, to enable autonomic controllers to satisfy service level objectives (SLOs) while more effectively utilizing IT resources. We show that with only minimal knowledge of application and system behaviors, our methodology can model the probability distribution of response time with a mean absolute error of less than 6% when compared with the measured response time distribution. We then demonstrate the usefulness of a probabilistic approach with case studies. We apply basic laws of probability to our model to investigate whether and how CPU allocation and contention affect application response time, correcting for their effects on CPU utilization. We find mean absolute differences of 8-10% between the modeled response time distributions of certain allocation states, and a similar difference when we add CPU contention. This methodology is general, and should also be applicable to non-CPU virtualized resources and other performance modeling problems.