Automated control of multiple virtualized resources

  • Authors:

  • Pradeep Padala;Kai-Yuan Hou;Kang G. Shin;Xiaoyun Zhu;Mustafa Uysal;Zhikui Wang;Sharad Singhal;Arif Merchant

  • Affiliations:

  • University of Michigan, Ann Arbor, MI, USA;University of Michigan, Ann Arbor, MI, USA;University of Michigan, Ann Arbor, MI, USA;VMware Inc., 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 4th ACM European conference on Computer systems
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

Abstract

Virtualized data centers enable sharing of resources among hosted applications. However, it is difficult to satisfy service-level objectives(SLOs) of applications on shared infrastructure, as application workloads and resource consumption patterns change over time. In this paper, we present AutoControl, a resource control system that automatically adapts to dynamic workload changes to achieve application SLOs. AutoControl is a combination of an online model estimator and a novel multi-input, multi-output (MIMO) resource controller. The model estimator captures the complex relationship between application performance and resource allocations, while the MIMO controller allocates the right amount of multiple virtualized resources to achieve application SLOs. Our experimental evaluation with RUBiS and TPC-W benchmarks along with production-trace-driven workloads indicates that AutoControl can detect and mitigate CPU and disk I/O bottlenecks that occur over time and across multiple nodes by allocating each resource accordingly. We also show that AutoControl can be used to provide service differentiation according to the application priorities during resource contention.