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VM capacity-aware scheduling within budget constraints in IaaS clouds

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VM capacity-aware scheduling within budget constraints in IaaS clouds. / Thanasias, V.; Lee, C.; Hanif, M. et al.

In: PLoS ONE, Vol. 11, No. 8, e0160456, 08.08.2016.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Thanasias, V, Lee, C, Hanif, M, Kim, E & Helal, S 2016, 'VM capacity-aware scheduling within budget constraints in IaaS clouds', PLoS ONE, vol. 11, no. 8, e0160456. https://doi.org/10.1371/journal.pone.0160456

APA

Vancouver

Thanasias V, Lee C, Hanif M, Kim E, Helal S. VM capacity-aware scheduling within budget constraints in IaaS clouds. PLoS ONE. 2016 Aug 8;11(8):e0160456. doi: 10.1371/journal.pone.0160456

Author

Thanasias, V. ; Lee, C. ; Hanif, M. et al. / VM capacity-aware scheduling within budget constraints in IaaS clouds. In: PLoS ONE. 2016 ; Vol. 11, No. 8.

Bibtex

@article{12b04f065514466caab784890e625020,
title = "VM capacity-aware scheduling within budget constraints in IaaS clouds",
abstract = "Recently, cloud computing has drawn significant attention from both industry and academia, bringing unprecedented changes to computing and information technology. The infrastructure-as-a-Service (IaaS) model offers new abilities such as the elastic provisioning and relinquishing of computing resources in response to workload fluctuations. However, because the demand for resources dynamically changes over time, the provisioning of resources in a way that a given budget is efficiently utilized while maintaining a sufficing performance remains a key challenge. This paper addresses the problem of task scheduling and resource provisioning for a set of tasks running on IaaS clouds; it presents novel provisioning and scheduling algorithms capable of executing tasks within a given budget, while minimizing the slowdown due to the budget constraint. Our simulation study demonstrates a substantial reduction up to 70% in the overall task slowdown rate by the proposed algorithms. {\textcopyright} 2016 Thanasias et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.",
keywords = "budget, running, algorithm, cloud computing, economics, theoretical model, workload, Algorithms, Cloud Computing, Models, Theoretical, Workload",
author = "V. Thanasias and C. Lee and M. Hanif and E. Kim and Sumi Helal",
year = "2016",
month = aug,
day = "8",
doi = "10.1371/journal.pone.0160456",
language = "English",
volume = "11",
journal = "PLoS ONE",
issn = "1932-6203",
publisher = "Public Library of Science",
number = "8",

}

RIS

TY - JOUR

T1 - VM capacity-aware scheduling within budget constraints in IaaS clouds

AU - Thanasias, V.

AU - Lee, C.

AU - Hanif, M.

AU - Kim, E.

AU - Helal, Sumi

PY - 2016/8/8

Y1 - 2016/8/8

N2 - Recently, cloud computing has drawn significant attention from both industry and academia, bringing unprecedented changes to computing and information technology. The infrastructure-as-a-Service (IaaS) model offers new abilities such as the elastic provisioning and relinquishing of computing resources in response to workload fluctuations. However, because the demand for resources dynamically changes over time, the provisioning of resources in a way that a given budget is efficiently utilized while maintaining a sufficing performance remains a key challenge. This paper addresses the problem of task scheduling and resource provisioning for a set of tasks running on IaaS clouds; it presents novel provisioning and scheduling algorithms capable of executing tasks within a given budget, while minimizing the slowdown due to the budget constraint. Our simulation study demonstrates a substantial reduction up to 70% in the overall task slowdown rate by the proposed algorithms. © 2016 Thanasias et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

AB - Recently, cloud computing has drawn significant attention from both industry and academia, bringing unprecedented changes to computing and information technology. The infrastructure-as-a-Service (IaaS) model offers new abilities such as the elastic provisioning and relinquishing of computing resources in response to workload fluctuations. However, because the demand for resources dynamically changes over time, the provisioning of resources in a way that a given budget is efficiently utilized while maintaining a sufficing performance remains a key challenge. This paper addresses the problem of task scheduling and resource provisioning for a set of tasks running on IaaS clouds; it presents novel provisioning and scheduling algorithms capable of executing tasks within a given budget, while minimizing the slowdown due to the budget constraint. Our simulation study demonstrates a substantial reduction up to 70% in the overall task slowdown rate by the proposed algorithms. © 2016 Thanasias et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

KW - budget

KW - running

KW - algorithm

KW - cloud computing

KW - economics

KW - theoretical model

KW - workload

KW - Algorithms

KW - Cloud Computing

KW - Models, Theoretical

KW - Workload

U2 - 10.1371/journal.pone.0160456

DO - 10.1371/journal.pone.0160456

M3 - Journal article

VL - 11

JO - PLoS ONE

JF - PLoS ONE

SN - 1932-6203

IS - 8

M1 - e0160456

ER -