research-article

SPEC Cloud™ IaaS 2016 Benchmark

Authors:

Salman Baset,

Marcio Silva,

Nicholas WakouAuthors Info & Claims

ICPE '17: Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering

Page 423

https://doi.org/10.1145/3030207.3053675

Published: 17 April 2017 Publication History

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Abstract

The SPEC Cloud (TM) IaaS2016 benchmark is the Standard Performance Evaluation Corporation's (SPEC) first benchmark suite to measure cloud performance. The benchmark suite's use is targeted at cloud providers, cloud consumers, hardware vendors, virtualization software vendors, application software vendors, and academic researchers. The benchmark addresses the performance of infrastructure-as-a-service (IaaS) cloud platforms. IaaS cloud platforms can either be public or private. The key metrics of the benchmark are scalability, elasticity, and mean instance provisioning time.

Reference

[1]

SPEC Cloud™ IaaS 2016 Benchmark. https://www.spec.org/cloud_iaas2016/, May 2003.

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Cited By

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Fattah SBouguettaya A(2024)Signature-based IaaS Performance Change DetectionACM Transactions on Internet Technology10.1145/370222825:1(1-21)Online publication date: 26-Oct-2024
https://dl.acm.org/doi/10.1145/3702228
Hu XBurgstaller BScholz B(2023)EVMTracer: Dynamic Analysis of the Parallelization and Redundancy Potential in the Ethereum Virtual MachineIEEE Access10.1109/ACCESS.2023.326727711(47159-47178)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3267277
Yang YKong XZhao LLi YZhang HLi JQi HLi K(2022)SDCBench: A Benchmark Suite for Workload Colocation and Evaluation in DatacentersIntelligent Computing10.34133/2022/98106912022Online publication date: Jan-2022
https://doi.org/10.34133/2022/9810691
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Index Terms

SPEC Cloud™ IaaS 2016 Benchmark
1. Computer systems organization
  1. Architectures
    1. Distributed architectures
      1. Cloud computing
2. General and reference
  1. Cross-computing tools and techniques
    1. Performance

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Information & Contributors

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Published In

ICPE '17: Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering

April 2017

450 pages

ISBN:9781450344043

DOI:10.1145/3030207

General Chairs:
Walter Binder
Università della Svizzera Italiana, Switzerland
,
Vittorio Cortellessa
Università dell'Aquila, Italy
,
Program Chairs:
Anne Koziolek
Karlsruhe Institute of Technology, Germany
,
Evgenia Smirni
College of William & Mary, USA
,
Meikel Poess
Oracle Corporation, USA

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 April 2017

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ICPE '17

Sponsor:

ICPE '17: ACM/SPEC International Conference on Performance Engineering

April 22 - 26, 2017

L'Aquila, Italy

Acceptance Rates

ICPE '17 Paper Acceptance Rate 27 of 83 submissions, 33%;

Overall Acceptance Rate 252 of 851 submissions, 30%

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Cited By

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Fattah SBouguettaya A(2024)Signature-based IaaS Performance Change DetectionACM Transactions on Internet Technology10.1145/370222825:1(1-21)Online publication date: 26-Oct-2024
https://dl.acm.org/doi/10.1145/3702228
Hu XBurgstaller BScholz B(2023)EVMTracer: Dynamic Analysis of the Parallelization and Redundancy Potential in the Ethereum Virtual MachineIEEE Access10.1109/ACCESS.2023.326727711(47159-47178)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3267277
Yang YKong XZhao LLi YZhang HLi JQi HLi K(2022)SDCBench: A Benchmark Suite for Workload Colocation and Evaluation in DatacentersIntelligent Computing10.34133/2022/98106912022Online publication date: Jan-2022
https://doi.org/10.34133/2022/9810691
Fattah SBouguettaya AMistry S(2022)Long-Term IaaS Selection Using Performance DiscoveryIEEE Transactions on Services Computing10.1109/TSC.2020.303667715:4(2129-2143)Online publication date: 1-Jul-2022
https://doi.org/10.1109/TSC.2020.3036677
Keskin RPileggi P(2022)Making the Cloud Monitor Real-Time Adaptive2022 IEEE Cloud Summit10.1109/CloudSummit54781.2022.00017(69-74)Online publication date: Oct-2022
https://doi.org/10.1109/CloudSummit54781.2022.00017
Zhang JJiang SWang KWang RLiu QYuan X(2022)Overview of Information System Testing Technology Under the “CLOUD + MIcroservices” ModeComputer and Communication Engineering10.1007/978-3-031-17422-3_6(62-74)Online publication date: 24-Sep-2022
https://doi.org/10.1007/978-3-031-17422-3_6
Liu ZZuo XLi ZHan R(2020)SparkAIBench: A Benchmark to Generate AI Workloads on SparkBenchmarking, Measuring, and Optimizing10.1007/978-3-030-49556-5_21(215-221)Online publication date: 9-Jun-2020
https://doi.org/10.1007/978-3-030-49556-5_21
Fattah SBouguettaya AMistry S(2019)Long-Term IaaS Provider Selection Using Short-Term Trial Experience2019 IEEE International Conference on Web Services (ICWS)10.1109/ICWS.2019.00058(304-311)Online publication date: Jul-2019
https://doi.org/10.1109/ICWS.2019.00058
van Eyk EIosup AAbad CGrohmann JEismann SWolter KKnottenbelt Wvan Hoorn ANambiar MKoziolek H(2018)A SPEC RG Cloud Group's Vision on the Performance Challenges of FaaS Cloud ArchitecturesCompanion of the 2018 ACM/SPEC International Conference on Performance Engineering10.1145/3185768.3186308(21-24)Online publication date: 2-Apr-2018
https://dl.acm.org/doi/10.1145/3185768.3186308
Zhao YFeng XChen NWang YYu YWang H(2018)A multi-dimensional hierarchical performance evaluation model for edge cloud platformProcedia Computer Science10.1016/j.procs.2018.03.013129(389-393)Online publication date: 2018
https://doi.org/10.1016/j.procs.2018.03.013
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