research-article

Evaluating the impact of fine-scale burstiness on cloud elasticity

Authors:

Srikumar Venugopal,

Anna LiuAuthors Info & Claims

SoCC '15: Proceedings of the Sixth ACM Symposium on Cloud Computing

Pages 250 - 261

https://doi.org/10.1145/2806777.2806846

Published: 27 August 2015 Publication History

Abstract

Elasticity is the defining feature of cloud computing. Performance analysts and adaptive system designers rely on representative benchmarks for evaluating elasticity for cloud applications under realistic reproducible workloads. A key feature of web workloads is burstiness or high variability at fine timescales. In this paper, we explore the innate interaction between fine-scale burstiness and elasticity and quantify the impact from the cloud consumer's perspective. We propose a novel methodology to model workloads with fine-scale burstiness so that they can resemble the empirical stylized facts of the arrival process. Through an experimental case study, we extract insights about the implications of fine-scale burstiness for elasticity penalty and adaptive resource scaling. Our findings demonstrate the detrimental effect of fine-scale burstiness on the elasticity of cloud applications.

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Cited By

Wang YYu JYu Z(2023)Resource scheduling techniques in cloud from a view of coordination: a holistic survey从协同视角论云资源调度技术：综述Frontiers of Information Technology & Electronic Engineering10.1631/FITEE.210029824:1(1-40)Online publication date: 23-Jan-2023
https://doi.org/10.1631/FITEE.2100298
Chairatana NChawuthai R(2023)Cloud Stateless Server Failover Prediction Using Machine Learning on Proactive System Metrics2023 18th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)10.1109/iSAI-NLP60301.2023.10354585(1-6)Online publication date: 27-Nov-2023
https://doi.org/10.1109/iSAI-NLP60301.2023.10354585
Kim IWang WQi YHumphrey M(2022) Forecasting Cloud Application Workloads With CloudInsight for Predictive Resource Management IEEE Transactions on Cloud Computing10.1109/TCC.2020.299801710:3(1848-1863)Online publication date: 1-Jul-2022
https://doi.org/10.1109/TCC.2020.2998017
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Evaluating the impact of fine-scale burstiness on cloud elasticity

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Published In

cover image ACM Conferences

SoCC '15: Proceedings of the Sixth ACM Symposium on Cloud Computing

August 2015

446 pages

ISBN:9781450336512

DOI:10.1145/2806777

General Chair:
Shahram Ghandeharizadeh
University of Southern California
,
Program Chairs:
Magdalena Balazinska
University of Washington
,
Michael J. Freedman
Princeton University
,
Publications Chair:
Sumita Barahmand
Microsoft

Copyright � 2015 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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Publication History

Published: 27 August 2015

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Amazon Web Services

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SoCC '15

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SoCC '15: ACM Symposium on Cloud Computing

August 27 - 29, 2015

Hawaii, Kohala Coast

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SoCC '15 Paper Acceptance Rate 34 of 157 submissions, 22%;

Overall Acceptance Rate 169 of 722 submissions, 23%

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Cited By

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https://doi.org/10.1631/FITEE.2100298
Chairatana NChawuthai R(2023)Cloud Stateless Server Failover Prediction Using Machine Learning on Proactive System Metrics2023 18th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)10.1109/iSAI-NLP60301.2023.10354585(1-6)Online publication date: 27-Nov-2023
https://doi.org/10.1109/iSAI-NLP60301.2023.10354585
Kim IWang WQi YHumphrey M(2022) Forecasting Cloud Application Workloads With CloudInsight for Predictive Resource Management IEEE Transactions on Cloud Computing10.1109/TCC.2020.299801710:3(1848-1863)Online publication date: 1-Jul-2022
https://doi.org/10.1109/TCC.2020.2998017
Kim IHwang JWang WHumphrey M(2022)Guaranteeing Performance SLAs of Cloud Applications Under Resource StormsIEEE Transactions on Cloud Computing10.1109/TCC.2020.298537210:2(1329-1343)Online publication date: 1-Apr-2022
https://doi.org/10.1109/TCC.2020.2985372
Birke RPerez JQiu ZBorkqvist MChen L(2021)sPARE: Partial Replication for Multi-Tier Applications in the CloudIEEE Transactions on Services Computing10.1109/TSC.2017.278084514:2(574-588)Online publication date: 1-Mar-2021
https://doi.org/10.1109/TSC.2017.2780845
Jayakumar VLee JKim IWang W(2020)A Self-Optimized Generic Workload Prediction Framework for Cloud Computing2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS)10.1109/IPDPS47924.2020.00085(779-788)Online publication date: May-2020
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Kim IHwang JWang WHumphrey M(2018)Orchestra: Guaranteeing Performance SLAs for Cloud Applications by Avoiding Resource Storms2018 17th International Symposium on Parallel and Distributed Computing (ISPDC)10.1109/ISPDC2018.2018.00017(53-60)Online publication date: Jun-2018
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Mencagli GTorquati MDanelutto MDe Matteis T(2017)Parallel Continuous Preference Queries over Out-of-Order and Bursty Data StreamsIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2017.267919728:9(2608-2624)Online publication date: 1-Sep-2017
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Zhu TBerger DHarchol-Balter M(2016)SNC-MeisterProceedings of the Seventh ACM Symposium on Cloud Computing10.1145/2987550.2987585(374-387)Online publication date: 5-Oct-2016
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