Managing Dynamic Enterprise and Urgent Workloads on Clouds Using Layered Queuing and Historical Performance Models
Managing Dynamic Enterprise and Urgent Workloads on Clouds Using Layered Queuing and Historical Performance Models
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.
cloud, performance modelling, HYDRA historical model, layered queuing, FireGrid
1479-1495
Bacigalupo, David A.
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van Hemert, Jano
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Chen, Xiaoyu
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Usmani, Asif
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Chester, Adam P.
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He, Ligang
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Dillenberger, Donna N.
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Wills, Gary
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Gilbert, Lester
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Jarvis, Stephen A.
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2011
Bacigalupo, David A.
bb171f60-8954-42f8-9604-0d28c6c85fd2
van Hemert, Jano
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Chen, Xiaoyu
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Usmani, Asif
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Chester, Adam P.
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He, Ligang
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Dillenberger, Donna N.
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Wills, Gary
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Gilbert, Lester
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Jarvis, Stephen A.
fef1d57e-4b08-40b7-a2d2-6f1abbd02162
Bacigalupo, David A., van Hemert, Jano, Chen, Xiaoyu, Usmani, Asif, Chester, Adam P., He, Ligang, Dillenberger, Donna N., Wills, Gary, Gilbert, Lester and Jarvis, Stephen A.
(2011)
Managing Dynamic Enterprise and Urgent Workloads on Clouds Using Layered Queuing and Historical Performance Models.
Simulation Modelling Practice & Theory, 19 (6), .
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.
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Published date: 2011
Keywords:
cloud, performance modelling, HYDRA historical model, layered queuing, FireGrid
Organisations:
Electronics & Computer Science, Electronic & Software Systems, IT Innovation
Identifiers
Local EPrints ID: 272007
URI: http://eprints.soton.ac.uk/id/eprint/272007
PURE UUID: 92368ee4-d5b6-47ee-afc4-b41f77588873
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Date deposited: 11 Feb 2011 06:44
Last modified: 15 Mar 2024 02:51
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Contributors
Author:
David A. Bacigalupo
Author:
Jano van Hemert
Author:
Xiaoyu Chen
Author:
Asif Usmani
Author:
Adam P. Chester
Author:
Ligang He
Author:
Donna N. Dillenberger
Author:
Gary Wills
Author:
Lester Gilbert
Author:
Stephen A. Jarvis
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