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

David A. Bacigalupo; Jano Van Hemert; Xiaoyu Chen; Asif Usmani; Adam P. Chester; Ligang He; Donna N. Dillenberger; Gary B. Wills; Lester Gilbert; Stephen 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

David A. Bacigalupo, Jano Van Hemert, Xiaoyu Chen, Asif Usmani, Adam P. Chester, Ligang He, Donna N. Dillenberger, Gary B. Wills, Lester Gilbert, Stephen A. Jarvis

Engineering and Physical Sciences

Research output: Contribution to journal › Article › peer-review

21 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.

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 - Jun 2011

Bibliographical note

Funding Information:
This work was sponsored in part by the JISC TeciRes project, the Technology Strategy Board FireGrid Project, The University of Edinburgh School of Informatics, the EPSRC DOPCHE Project (GR/S03058/01). We gratefully acknowledge the assistance during current and earlier versions of this work of: M. Atkinson, G. Beckett, J. Koetsier, S. Koo, T. Liu, I. Mitrani, A. van Moorsel, G. Nudd, S. Potter, G. Pringle, J. Slegers, C. Smith, D. Spooner, N. Thomas, J. Torero, S. Welch, W. J. Xue, S. J. Pennycook and all FireGrid team members not mentioned here.

Keywords

Cloud
FireGrid
HYDRA historical model
Layered queuing
Performance modelling

ASJC Scopus subject areas

Software
Modelling and Simulation
Hardware and Architecture

Access to Document

10.1016/j.simpat.2011.01.007

Cite this

Bacigalupo, D. A., Van Hemert, J., Chen, X., Usmani, A., 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

@article{9a8df48191974e699f8c205ee0ed254d,

title = "Managing dynamic enterprise and urgent workloads on clouds using layered queuing and historical performance models",

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.",

keywords = "Cloud, FireGrid, HYDRA historical model, Layered queuing, Performance modelling",

author = "Bacigalupo, {David A.} and {Van Hemert}, Jano and Xiaoyu Chen and Asif Usmani and Chester, {Adam P.} and Ligang He and Dillenberger, {Donna N.} and Wills, {Gary B.} and Lester Gilbert and Jarvis, {Stephen A.}",

note = "Funding Information: This work was sponsored in part by the JISC TeciRes project, the Technology Strategy Board FireGrid Project, The University of Edinburgh School of Informatics, the EPSRC DOPCHE Project (GR/S03058/01). We gratefully acknowledge the assistance during current and earlier versions of this work of: M. Atkinson, G. Beckett, J. Koetsier, S. Koo, T. Liu, I. Mitrani, A. van Moorsel, G. Nudd, S. Potter, G. Pringle, J. Slegers, C. Smith, D. Spooner, N. Thomas, J. Torero, S. Welch, W. J. Xue, S. J. Pennycook and all FireGrid team members not mentioned here.",

year = "2011",

month = jun,

doi = "10.1016/j.simpat.2011.01.007",

language = "English",

volume = "19",

pages = "1479--1495",

journal = "Simulation Modelling Practice and Theory",

issn = "1569-190X",

publisher = "Elsevier",

number = "6",

}

TY - JOUR

T1 - Managing dynamic enterprise and urgent workloads on clouds using layered queuing and historical performance models

AU - Bacigalupo, David A.

AU - Van Hemert, Jano

AU - Chen, Xiaoyu

AU - Usmani, Asif

AU - Chester, Adam P.

AU - He, Ligang

AU - Dillenberger, Donna N.

AU - Wills, Gary B.

AU - Gilbert, Lester

AU - Jarvis, Stephen A.

N1 - Funding Information: This work was sponsored in part by the JISC TeciRes project, the Technology Strategy Board FireGrid Project, The University of Edinburgh School of Informatics, the EPSRC DOPCHE Project (GR/S03058/01). We gratefully acknowledge the assistance during current and earlier versions of this work of: M. Atkinson, G. Beckett, J. Koetsier, S. Koo, T. Liu, I. Mitrani, A. van Moorsel, G. Nudd, S. Potter, G. Pringle, J. Slegers, C. Smith, D. Spooner, N. Thomas, J. Torero, S. Welch, W. J. Xue, S. J. Pennycook and all FireGrid team members not mentioned here.

PY - 2011/6

Y1 - 2011/6

N2 - 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.

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.

KW - Cloud

KW - FireGrid

KW - HYDRA historical model

KW - Layered queuing

KW - Performance modelling

UR - http://www.scopus.com/inward/record.url?scp=79955654404&partnerID=8YFLogxK

U2 - 10.1016/j.simpat.2011.01.007

DO - 10.1016/j.simpat.2011.01.007

M3 - Article

AN - SCOPUS:79955654404

SN - 1569-190X

VL - 19

SP - 1479

EP - 1495

JO - Simulation Modelling Practice and Theory

JF - Simulation Modelling Practice and Theory

IS - 6

ER -

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

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this