research-article

Transactional auto scaler: elastic scaling of in-memory transactional data grids

Authors:

Sebastiano Peluso,

Francesco QuagliaAuthors Info & Claims

ICAC '12: Proceedings of the 9th international conference on Autonomic computing

Pages 125 - 134

https://doi.org/10.1145/2371536.2371559

Published: 18 September 2012 Publication History

Abstract

In this paper we introduce TAS (Transactional Auto Scaler), a system for automating elastic-scaling of in-memory transactional data grids, such as NoSQL data stores or Distributed Transactional Memories. Applications of TAS range from on-line self-optimization of in-production applications to automatic generation of QoS/cost driven elastic scaling policies, and support for what-if analysis on the scalability of transactional applications.

The key innovation at the core of TAS is a novel performance forecasting methodology that relies on the joint usage of analytical modeling and machine-learning. By exploiting these two, classically competing, methodologies in a synergic fashion, TAS achieves the best of the two worlds, namely high extrapolation power and good accuracy even when faced with complex workloads deployed over public cloud infrastructures.

We demonstrate the accuracy and feasibility of TAS via an extensive experimental study based on a fully fledged prototype implementation, integrated with a popular open-source transactional in-memory data store (Red Hat's Infinispan), and industry-standard benchmarks generating a breadth of heterogeneous workloads.

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Cited By

Tay YApte VDi Marco ALitoiu MMerseguer J(2019)Lessons from Teaching Analytical Performance ModelingCompanion of the 2019 ACM/SPEC International Conference on Performance Engineering10.1145/3302541.3311527(79-84)Online publication date: 27-Mar-2019
https://dl.acm.org/doi/10.1145/3302541.3311527
Bencomo NGötz SSong H(2019)[email protected]Software and Systems Modeling (SoSyM)10.1007/s10270-018-00712-x18:5(3049-3082)Online publication date: 1-Oct-2019
https://dl.acm.org/doi/10.1007/s10270-018-00712-x
Tay Y(2018)Analytical Performance Modeling for Computer Systems, Third EditionSynthesis Lectures on Computer Science10.2200/S00859ED3V01Y201806CSL0107:1(1-171)Online publication date: 23-Jul-2018
https://doi.org/10.2200/S00859ED3V01Y201806CSL010
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Index Terms

Transactional auto scaler: elastic scaling of in-memory transactional data grids
1. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis
      1. Modeling methodologies
2. General and reference
  1. Cross-computing tools and techniques

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Published In

cover image ACM Conferences

ICAC '12: Proceedings of the 9th international conference on Autonomic computing

September 2012

222 pages

ISBN:9781450315203

DOI:10.1145/2371536

General Chair:
Dejan Milojicic
HP Labs
,
Program Chairs:
Dongyan Xu
Purdue University
,
Vanish Talwar
HP Labs

Copyright © 2012 ACM.

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Published: 18 September 2012

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ICAC '12: 9th International Conference on Autonomic Computing

September 18 - 20, 2012

California, San Jose, USA

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Cited By

Tay YApte VDi Marco ALitoiu MMerseguer J(2019)Lessons from Teaching Analytical Performance ModelingCompanion of the 2019 ACM/SPEC International Conference on Performance Engineering10.1145/3302541.3311527(79-84)Online publication date: 27-Mar-2019
https://dl.acm.org/doi/10.1145/3302541.3311527
Bencomo NGötz SSong H(2019)[email protected]Software and Systems Modeling (SoSyM)10.1007/s10270-018-00712-x18:5(3049-3082)Online publication date: 1-Oct-2019
https://dl.acm.org/doi/10.1007/s10270-018-00712-x
Tay Y(2018)Analytical Performance Modeling for Computer Systems, Third EditionSynthesis Lectures on Computer Science10.2200/S00859ED3V01Y201806CSL0107:1(1-171)Online publication date: 23-Jul-2018
https://doi.org/10.2200/S00859ED3V01Y201806CSL010
Ghosh MXu LGupta I(2018)Resource Management: Performance Assuredness in Distributed Cloud Computing via Online ReconfigurationsAssured Cloud Computing10.1002/9781119428497.ch6(160-236)Online publication date: 20-Dec-2018
https://doi.org/10.1002/9781119428497.ch6
Martinez RLopes ARodrigues LShin SShin DLencastre M(2017)Automated generation of policies to support elastic scaling in cloud environmentsProceedings of the Symposium on Applied Computing10.1145/3019612.3019658(450-455)Online publication date: 3-Apr-2017
https://dl.acm.org/doi/10.1145/3019612.3019658
Liu YGureya DAl-Shishtawy AVlassov V(2017)OnlineElastManCluster Computing10.1007/s10586-017-0899-z20:3(1977-1994)Online publication date: 1-Sep-2017
https://dl.acm.org/doi/10.1007/s10586-017-0899-z
Diegues NRomano P(2016)STI-BT: A Scalable Transactional IndexIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2015.248526727:8(2408-2421)Online publication date: 1-Aug-2016
https://doi.org/10.1109/TPDS.2015.2485267
Rameshan NLiu YNavarro LVlassov V(2016)Elastic Scaling in the Cloud: A Multi-tenant Perspective2016 IEEE 36th International Conference on Distributed Computing Systems Workshops (ICDCSW)10.1109/ICDCSW.2016.36(25-30)Online publication date: Jun-2016
https://doi.org/10.1109/ICDCSW.2016.36
Rameshan NLiu YNavarro LVlassov V(2016)Augmenting Elasticity Controllers for Improved Accuracy2016 IEEE International Conference on Autonomic Computing (ICAC)10.1109/ICAC.2016.60(117-126)Online publication date: Jul-2016
https://doi.org/10.1109/ICAC.2016.60
Pundir MKumar MLeslie LGupta ICampbell R(2016)Supporting On-demand Elasticity in Distributed Graph Processing2016 IEEE International Conference on Cloud Engineering (IC2E)10.1109/IC2E.2016.31(12-21)Online publication date: Apr-2016
https://doi.org/10.1109/IC2E.2016.31
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