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

doi:10.1016/j.simpat.2011.01.007

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 publication › Conference article › Academic research › peer-review

23 Citations (Scopus)

Abstract

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 language	English
Pages (from-to)	1479-1495
Number of pages	17
Journal	Simulation Modelling Practice and Theory
Volume	19
Issue number	6
DOIs	https://doi.org/10.1016/j.simpat.2011.01.007
Publication status	Published - 1 Jun 2011
Externally published	Yes

Keywords

Cloud
FireGrid
HYDRA historical model
Layered queuing
Performance modelling

ASJC Scopus subject areas

Software
Modelling and Simulation
Hardware and Architecture

More information

10.1016/j.simpat.2011.01.007

Cite this

Bacigalupo, D. A., Van Hemert, J., Chen, X., Usmani, A. S., Chester, A. P., He, L., Dillenberger, D. N., Wills, G. B., Gilbert, L., & Jarvis, S. A. (2011). Managing dynamic enterprise and urgent workloads on clouds using layered queuing and historical performance models. Simulation Modelling Practice and Theory, 19(6), 1479-1495. https://doi.org/10.1016/j.simpat.2011.01.007

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abstract = "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. {\textcopyright} 2011 Elsevier B.V. All rights reserved.",

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AU - Chester, A.P.

AU - He, L.

AU - Dillenberger, D.N.

AU - Wills, G.B.

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AU - Jarvis, S.A.

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AB - 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.

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KW - HYDRA historical model

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KW - Performance modelling

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

Abstract

Keywords

ASJC Scopus subject areas

More information

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