research-article

How a consumer can measure elasticity for cloud platforms

Authors:

Anna LiuAuthors Info & Claims

ICPE '12: Proceedings of the 3rd ACM/SPEC International Conference on Performance Engineering

Pages 85 - 96

https://doi.org/10.1145/2188286.2188301

Published: 22 April 2012 Publication History

Abstract

One major benefit claimed for cloud computing is elasticity: the cost to a consumer of computation can grow or shrink with the workload. This paper offers improved ways to quantify the elasticity concept, using data available to the consumer. We define a measure that reflects the financial penalty to a particular consumer, from under-provisioning (leading to unacceptable latency or unmet demand) or over-provisioning (paying more than necessary for the resources needed to support a workload). We have applied several workloads to a public cloud; from our experiments we extract insights into the characteristics of a platform that influence its elasticity. We explore the impact of the rules used to increase or decrease capacity.

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Cited By

Alidoost Alanagh YFirouzi MRasouli Kenari AShamsi M(2023) Introducing an adaptive model for auto‐scaling cloud computing based on workload classification Concurrency and Computation: Practice and Experience10.1002/cpe.772035:22Online publication date: 12-Apr-2023
https://doi.org/10.1002/cpe.7720
Souza RSantos MBonfim MDias KFernandes S(2022)KDN-Based Fault-Tolerant Scheduling for VNFs in Data CentersIEEE Transactions on Network and Service Management10.1109/TNSM.2022.318514419:4(4905-4917)Online publication date: Dec-2022
https://doi.org/10.1109/TNSM.2022.3185144
Hummaida APaton NSakellariou R(2022)A hierarchical decentralized architecture to enable adaptive scalable virtual machine migrationConcurrency and Computation: Practice and Experience10.1002/cpe.748735:2Online publication date: 18-Nov-2022
https://doi.org/10.1002/cpe.7487
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Published In

cover image ACM Conferences

ICPE '12: Proceedings of the 3rd ACM/SPEC International Conference on Performance Engineering

April 2012

362 pages

ISBN:9781450312028

DOI:10.1145/2188286

General Chairs:
David Kaeli
Northeastern University, USA
,
Jerry Rolia
Hewlett Packard Labs, USA
,
Program Chairs:
Lizy John
University of Texas at Austin, USA
,
Diwakar Krishnamurthy
University of Calgary, Canada

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGMETRICS: ACM Special Interest Group on Measurement and Evaluation
SIGSOFT: ACM Special Interest Group on Software Engineering
SPEC: SPEC Research Group

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 April 2012

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ICPE'12

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ICPE'12: Third Joint WOSP/SIPEW International Conference on Performance Engineering

April 22 - 25, 2012

Massachusetts, Boston, USA

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Overall Acceptance Rate 252 of 851 submissions, 30%

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Cited By

Alidoost Alanagh YFirouzi MRasouli Kenari AShamsi M(2023) Introducing an adaptive model for auto‐scaling cloud computing based on workload classification Concurrency and Computation: Practice and Experience10.1002/cpe.772035:22Online publication date: 12-Apr-2023
https://doi.org/10.1002/cpe.7720
Souza RSantos MBonfim MDias KFernandes S(2022)KDN-Based Fault-Tolerant Scheduling for VNFs in Data CentersIEEE Transactions on Network and Service Management10.1109/TNSM.2022.318514419:4(4905-4917)Online publication date: Dec-2022
https://doi.org/10.1109/TNSM.2022.3185144
Hummaida APaton NSakellariou R(2022)A hierarchical decentralized architecture to enable adaptive scalable virtual machine migrationConcurrency and Computation: Practice and Experience10.1002/cpe.748735:2Online publication date: 18-Nov-2022
https://doi.org/10.1002/cpe.7487
Bharanidharan GJayalakshmi S(2021)Elastic Resource Allocation, Provisioning and Models Classification on Cloud Computing A Literature Review2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS)10.1109/ICACCS51430.2021.9442018(1909-1915)Online publication date: 19-Mar-2021
https://doi.org/10.1109/ICACCS51430.2021.9442018
Aslanpour MToosi ATaheri JGaire R(2021)AutoScaleSim: A simulation toolkit for auto-scaling Web applications in cloudsSimulation Modelling Practice and Theory10.1016/j.simpat.2020.102245108(102245)Online publication date: Apr-2021
https://doi.org/10.1016/j.simpat.2020.102245
Shahzad MAslam MSajjad WImran M(2020)Optimize Elasticity in Cloud Computing using Container Based VirtualizationInternational Journal of Innovations in Science and Technology10.33411/ijist/2020020101Online publication date: 1-Jan-2020
https://doi.org/10.33411/ijist/2020020101
Kuhlenkamp JWerner SBorges MErnst D(2020)All but oneACM SIGAPP Applied Computing Review10.1145/3429204.342920520:3(5-19)Online publication date: 8-Oct-2020
https://dl.acm.org/doi/10.1145/3429204.3429205
Kuhlenkamp JWerner SBorges MErnst DWenzel DHung CCerny TShin DBechini A(2020)Benchmarking elasticity of FaaS platforms as a foundation for objective-driven design of serverless applicationsProceedings of the 35th Annual ACM Symposium on Applied Computing10.1145/3341105.3373948(1576-1585)Online publication date: 30-Mar-2020
https://dl.acm.org/doi/10.1145/3341105.3373948
Souza RDias KFernandes S(2020)NFV Data Centers: A Systematic ReviewIEEE Access10.1109/ACCESS.2020.29735688(51713-51735)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2973568
Poggi NCuevas-Vicenttín VBerral JFenech TGómez GBrini DMontero ACarrera DMinhas UBlakeley JKossmann DRamakrishnan RSzyperski C(2020)Benchmarking Elastic Cloud Big Data Services Under SLA ConstraintsPerformance Evaluation and Benchmarking for the Era of Cloud(s)10.1007/978-3-030-55024-0_1(1-18)Online publication date: 30-Jul-2020
https://doi.org/10.1007/978-3-030-55024-0_1
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