research-article

Transactional Auto Scaler: Elastic Scaling of Replicated In-Memory Transactional Data Grids

Authors:

Sebastiano Peluso,

Francesco QuagliaAuthors Info & Claims

ACM Transactions on Autonomous and Adaptive Systems (TAAS), Volume 9, Issue 2

Article No.: 11, Pages 1 - 32

https://doi.org/10.1145/2620001

Published: 01 July 2014 Publication History

Abstract

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

In this article, we present the key innovation at the core of TAS, namely, a novel performance forecasting methodology that relies on the joint usage of analytical modeling and machine learning. By exploiting these two classically competing approaches 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’s performance forecasting methodology via an extensive experimental study based on a fully fledged prototype implementation integrated with a popular open-source in-memory transactional data grid (Red Hat’s Infinispan) and industry-standard benchmarks generating a breadth of heterogeneous workloads.

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Transactional Auto Scaler: Elastic Scaling of Replicated In-Memory Transactional Data Grids

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Published In

cover image ACM Transactions on Autonomous and Adaptive Systems

ACM Transactions on Autonomous and Adaptive Systems Volume 9, Issue 2

July 2014

146 pages

ISSN:1556-4665

EISSN:1556-4703

DOI:10.1145/2642710

Editors:
Manish Parashar
Rutgers University, USA
,
Franco Zambonelli
University of Modena e Reggio Emilia, Italy

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Copyright © 2014 ACM.

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Association for Computing Machinery

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Publication History

Published: 01 July 2014

Accepted: 01 March 2014

Revised: 01 October 2013

Received: 01 May 2013

Published in TAAS Volume 9, Issue 2

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Cited By

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https://doi.org/10.1093/comjnl/bxab131
Arun BPeluso SPalmieri RLosa GRavindran B(2020)Taming the Contention in Consensus-based Distributed SystemsIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2020.2970186(1-1)Online publication date: 2020
https://doi.org/10.1109/TDSC.2020.2970186
Ibeid HMeng SDobon OOlson LGropp W(2019)Learning with Analytical Models2019 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)10.1109/IPDPSW.2019.00128(778-786)Online publication date: May-2019
https://doi.org/10.1109/IPDPSW.2019.00128
Calegari RCiatto GMariani SDenti EOmicini A(2018)LPaaS as Micro-Intelligence: Enhancing IoT with Symbolic ReasoningBig Data and Cognitive Computing10.3390/bdcc20300232:3(23)Online publication date: 3-Aug-2018
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Economo SSilvestri EDi Sanzo PPellegrin AQuaglia F(2018)Model-Based Proactive Read-Validation in Transaction Processing Systems2018 IEEE 24th International Conference on Parallel and Distributed Systems (ICPADS)10.1109/PADSW.2018.8644605(481-488)Online publication date: Dec-2018
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Wojciechowski PKobus TKokocinski M(2017)State-Machine and Deferred-Update ReplicationIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2016.259042228:3(891-904)Online publication date: 1-Mar-2017
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Castro DRomano PDidona DZwaenepoel W(2017)An Analytical Model of Hardware Transactional Memory2017 IEEE 25th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS)10.1109/MASCOTS.2017.29(221-231)Online publication date: Sep-2017
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Ataie EGianniti EArdagna DMovaghar A(2016)A Combined Analytical Modeling Machine Learning Approach for Performance Prediction of MapReduce Jobs in Cloud Environment2016 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)10.1109/SYNASC.2016.072(431-439)Online publication date: Sep-2016
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Singh SChana I(2015)QoS-Aware Autonomic Resource Management in Cloud ComputingACM Computing Surveys10.1145/284388948:3(1-46)Online publication date: 22-Dec-2015
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Didona DRomano PJohn LSmith CSachs KLladó C(2015)Hybrid Machine Learning/Analytical Models for Performance PredictionProceedings of the 6th ACM/SPEC International Conference on Performance Engineering10.1145/2668930.2688823(341-344)Online publication date: 28-Jan-2015
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