Workload Analysis and Demand Prediction of Enterprise Data Center Applications

  • Authors:
  • Daniel Gmach;Jerry Rolia;Ludmila Cherkasova;Alfons Kemper

  • Affiliations:
  • Technische Universität München, 85748 Garching, Germany. daniel.gmach@in.tum.de;Hewlett-Packard Laboratories, Palo Alto, CA, USA. jerry.rolia@hp.com;Hewlett-Packard Laboratories, Palo Alto, CA, USA. lucy.cherkasova@hp.com;Technische Universität München, 85748 Garching, Germany. alfons.kemper@in.tum.de

  • Venue:
  • IISWC '07 Proceedings of the 2007 IEEE 10th International Symposium on Workload Characterization
  • Year:
  • 2007

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Abstract

Advances in virtualization technology are enabling the creation of resource pools of servers that permit multiple application workloads to share each server in the pool. Understanding the nature of enterprise workloads is crucial to properly designing and provisioning current and future services in such pools. This paper considers issues of workload analysis, performance modeling, and capacity planning. Our goal is to automate the efficient use of resource pools when hosting large numbers of enterprise services. We use a trace based approach for capacity management that relies on i) the characterization of workload demand patterns, ii) the generation of synthetic workloads that predict future demands based on the patterns, and iii) a workload placement recommendation service. The accuracy of capacity planning predictions depends on our ability to characterize workload demand patterns, to recognize trends for expected changes in future demands, and to reflect business forecasts for otherwise unexpected changes in future demands. A workload analysis demonstrates the burstiness and repetitive nature of enterprise workloads. Workloads are automatically classified according to their periodic behavior. The similarity among repeated occurrences of patterns is evaluated. Synthetic workloads are generated from the patterns in a manner that maintains the periodic nature, burstiness, and trending behavior of the workloads. A case study involving six months of data for 139 enterprise applications is used to apply and evaluate the enterprise workload analysis and related capacity planning methods.