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PerfEnforce Demonstration: Data Analytics with Performance Guarantees

Authors:

Jennifer Ortiz,

Brendan Lee,

Magdalena BalazinskaAuthors Info & Claims

SIGMOD '16: Proceedings of the 2016 International Conference on Management of Data

Pages 2141 - 2144

https://doi.org/10.1145/2882903.2899402

Published: 26 June 2016 Publication History

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Abstract

We demonstrate PerfEnforce, a dynamic scaling engine for analytics services. PerfEnforce automatically scales a cluster of virtual machines in order to minimize costs while probabilistically meeting the query runtime guarantees offered by a performance-oriented service level agreement (SLA). The demonstration will show three families of dynamic scaling algorithms --feedback control, reinforcement learning, and online machine learning--and will enable attendees to change tuning parameters, performance thresholds, and workloads to compare and contrast the algorithms in different settings.

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Cited By

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Tan VAziz KRazavi S(2022)Data Analytics for Effective Project Management in the Oil and Gas IndustryProceedings of the International Conference on Technology and Innovation Management (ICTIM 2022)10.2991/978-94-6463-080-0_20(233-242)Online publication date: 24-Dec-2022
https://doi.org/10.2991/978-94-6463-080-0_20
Zhu YInterlandi MRoy ADas KPatel HBag MSharma HJindal A(2021)PhoebeProceedings of the VLDB Endowment10.14778/3476249.347629814:11(2505-2518)Online publication date: 27-Oct-2021
https://dl.acm.org/doi/10.14778/3476249.3476298
Cetorelli VAtzeni PCrescenzi VMilicchio F(2021)The smallest extraction problemProceedings of the VLDB Endowment10.14778/3476249.347629314:11(2445-2458)Online publication date: 27-Oct-2021
https://dl.acm.org/doi/10.14778/3476249.3476293
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Index Terms

PerfEnforce Demonstration: Data Analytics with Performance Guarantees
1. Information systems
  1. Data management systems
    1. Database management system engines
      1. Parallel and distributed DBMSs

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Published In

SIGMOD '16: Proceedings of the 2016 International Conference on Management of Data

June 2016

2300 pages

ISBN:9781450335317

DOI:10.1145/2882903

General Chairs:
Fatma Özcan
IBM Research, USA
,
Georgia Koutrika
HP Labs, USA
,
Program Chair:
Sam Madden
Massachusetts Institute of Technology, 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 the author(s) 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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Published: 26 June 2016

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SIGMOD/PODS'16: International Conference on Management of Data

June 26 - July 1, 2016

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Tan VAziz KRazavi S(2022)Data Analytics for Effective Project Management in the Oil and Gas IndustryProceedings of the International Conference on Technology and Innovation Management (ICTIM 2022)10.2991/978-94-6463-080-0_20(233-242)Online publication date: 24-Dec-2022
https://doi.org/10.2991/978-94-6463-080-0_20
Zhu YInterlandi MRoy ADas KPatel HBag MSharma HJindal A(2021)PhoebeProceedings of the VLDB Endowment10.14778/3476249.347629814:11(2505-2518)Online publication date: 27-Oct-2021
https://dl.acm.org/doi/10.14778/3476249.3476298
Cetorelli VAtzeni PCrescenzi VMilicchio F(2021)The smallest extraction problemProceedings of the VLDB Endowment10.14778/3476249.347629314:11(2445-2458)Online publication date: 27-Oct-2021
https://dl.acm.org/doi/10.14778/3476249.3476293
Cen LMarcus RMao HGottschlich JAlizadeh MKraska TSen KNaik M(2020)Learned garbage collectionProceedings of the 4th ACM SIGPLAN International Workshop on Machine Learning and Programming Languages10.1145/3394450.3397469(38-44)Online publication date: 15-Jun-2020
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Marcus RZhang CYu SKao GPapaemmanouil O(2019)NashDBProceedings of the VLDB Endowment10.14778/3352063.335207712:12(1830-1833)Online publication date: 1-Aug-2019
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Giannakouris VFernandez ASimitsis ABabu SBoncz PManegold SAilamaki ADeshpande AKraska T(2019)Cost-Effective, Workload-Adaptive Migration of Big Data Applications to the CloudProceedings of the 2019 International Conference on Management of Data10.1145/3299869.3320240(1909-1912)Online publication date: 25-Jun-2019
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Farhana SLajis ALong Z(2019)Binary Algorithm for Big Data Management and Analytics of MyRA Data2019 IEEE International Conference on Smart Instrumentation, Measurement and Application (ICSIMA)10.1109/ICSIMA47653.2019.9057309(1-4)Online publication date: Aug-2019
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Shacham OGottesman YBergman ABortnikov EHillel EKeidar I(2018)Taking omid to the cloudsProceedings of the VLDB Endowment10.14778/3229863.322986811:12(1795-1808)Online publication date: 1-Aug-2018
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Li SGerver PMacMillan JDebrunner DMarshall WWu K(2018)Challenges and experiences in building an efficient apache beam runner for IBM streamsProceedings of the VLDB Endowment10.14778/3229863.322986411:12(1742-1754)Online publication date: 1-Aug-2018
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Marcus RPapaemmanouil OSemenova SGarber SDas GJermaine CBernstein P(2018)NashDBProceedings of the 2018 International Conference on Management of Data10.1145/3183713.3196935(1253-1267)Online publication date: 27-May-2018
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An Integrated Approach for Specifying and Enforcing SLAs for Cloud Services

An Agent Based Negotiation Protocol for Cloud Service Level Agreements

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REST-Based SLA Management for Cloud Applications

An Integrated Approach for Specifying and Enforcing SLAs for Cloud Services

An Agent Based Negotiation Protocol for Cloud Service Level Agreements

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