Managing dynamic enterprise and urgent workloads on clouds using layered queuing and historical performance models

D.A. Bacigalupo, J. Van Hemert, X. Chen, Asif Sohail Usmani, A.P. Chester, L. He, D.N. Dillenberger, G.B. Wills, L. Gilbert, S.A. Jarvis

Research output: Journal article publicationConference articleAcademic researchpeer-review

22 Citations (Scopus)


The automatic allocation of enterprise workload to resources can be enhanced by being able to make what-if response time predictions whilst different allocations are being considered. We experimentally investigate an historical and a layered queuing performance model and show how they can provide a good level of support for a dynamic-urgent cloud environment. Using this we define, implement and experimentally investigate the effectiveness of a prediction-based cloud workload and resource management algorithm. Based on these experimental analyses we: (i) comparatively evaluate the layered queuing and historical techniques; (ii) evaluate the effectiveness of the management algorithm in different operating scenarios; and (iii) provide guidance on using prediction-based workload and resource management. © 2011 Elsevier B.V. All rights reserved.
Original languageEnglish
Pages (from-to)1479-1495
Number of pages17
JournalSimulation Modelling Practice and Theory
Issue number6
Publication statusPublished - 1 Jun 2011
Externally publishedYes


  • Cloud
  • FireGrid
  • HYDRA historical model
  • Layered queuing
  • Performance modelling

ASJC Scopus subject areas

  • Software
  • Modelling and Simulation
  • Hardware and Architecture

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