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.

References

[1]

M. Bennani and D. Menasce. Resource allocation for autonomic data centers using analytic performance models. In Proc. of the International Conference on Autonomic Computing (ICAC), 2005.

Digital Library

[2]

H. Berenson, P. Bernstein, J. Gray, J. Melton, E. O'Neil, and P. O'Neil. A critique of ansi sql isolation levels. In Proc. of the ACM SIGMOD International Conference on Management of Data, 1995.

Digital Library

[3]

P. A. Bernstein, V. Hadzilacos, and N. Goodman. Concurrency control and recovery in database systems. 1986.

Digital Library

[4]

U. N. Bhat, M. Shalaby, and M. J. Fischer. Approximation techniques in the solution of queueing problems. Naval Research Logistics Quarterly, 1979.

[5]

C. M. Bishop. Pattern Recognition and Machine Learning (Information Science and Statistics). 2007.

Digital Library

[6]

F. Chang, J. Dean, S. Ghemawat, W. C. Hsieh, D. A. Wallach, M. Burrows, T. Chandra, A. Fikes, and R. E. Gruber. Bigtable: a distributed storage system for structured data. In Proc. of the USENIX Symposium on Operating Systems Design and Implementation (OSDI), 2006.

Digital Library

[7]

J. Chen, G. Soundararajan, and C. Amza. Autonomic provisioning of backend databases in dynamic content web servers. In Proc. of the International Conference on Autonomic Computing (ICAC), 2006.

Digital Library

[8]

B. Ciciani, D. M. Dias, and P. S. Yu. Analysis of replication in distributed database systems. IEEE Transactions on Knowledge and Data Engineering, 2(2), 1990.

Digital Library

[9]

Y. Dai, Y. Luo, Z. Li, and Z. Wang. A new adaptive cusum control chart for detecting the multivariate process mean. Quality and Reliability Engineering International, 27(7), 2011.

[10]

P. di Sanzo, B. Ciciani, F. Quaglia, and P. Romano. A performance model of multi-version concurrency control. In Proc. of the International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS), 2008.

[11]

P. di Sanzo, B. Ciciani, F. Quaglia, and P. Romano. Analytical modelling of commit-time-locking algorithms for software transactional memories. In Proc. of the International Computer Measurement Group Conference (CMG), 2010.

[12]

P. di Sanzo, R. Palmieri, B. Ciciani, F. Quaglia, and P. Romano. Analytical modeling of lock-based concurrency control with arbitrary transaction data access patterns. In Proc. of WOSP/SIPEW International Conference on Performance Engineering (ICPE), 2010.

Digital Library

[13]

D. Dice, O. Shalev, and N. Shavit. Transactional locking ii. In Proc. of the International Symposium on Distributed Computing (DISC), 2006.

Digital Library

[14]

D. Didona, P. Romano, S. Peluso, and F. Quaglia. Transactional auto scaler: Elastic scaling of in-memory transactional data grids. Technical Report 50/2011, INESC-ID, December 2011.

[15]

S. Ghanbari, G. Soundararajan, J. Chen, and C. Amza. Adaptive learning of metric correlations for temperature-aware database provisioning. In Proc. of the International Conference on Autonomic Computing (ICAC), 2007.

Digital Library

[16]

J. Gray, P. Helland, P. O'Neil, and D. Shasha. The dangers of replication and a solution. In Proc. of the ACM SIGMOD International Conference on Management of Data, 1996.

Digital Library

[17]

H. Herodotou, F. Dong, and S. Babu. No one (cluster) size fits all: automatic cluster sizing for data-intensive analytics. In Proc. of the ACM Symposium on Cloud Computing (SOCC), 2011.

Digital Library

[18]

L. Kleinrock. Theory, Volume 1, Queueing Systems. 1975.

Digital Library

[19]

A. Lakshman and P. Malik. Cassandra: a decentralized structured storage system. SIGOPS Operating System Review, 44, 2010.

Digital Library

[20]

J. D. C. Little. A proof for the queuing formula: L= łambda w. Operations Research, 9(3), 1961.

[21]

D. A. Menascé and T. Nakanishi. Performance evaluation of a two-phase commit based protocol for ddbs. In Proc. of the ACM SIGACT-SIGMOD symposium on Principles of Database Systems (PODS), 1982.

Digital Library

[22]

R. Nathuji, A. Kansal, and A. Ghaffarkhah. Q-clouds: managing performance interference effects for qos-aware clouds. In Proc. of the ACM European Conference on Computer Systems (EuroSys), 2010.

Digital Library

[23]

P. Padala, K.-Y. Hou, K. G. Shin, X. Zhu, M. Uysal, Z. Wang, S. Singhal, and A. Merchant. Automated control of multiple virtualized resources. In Proc. of the ACM European conference on Computer Systems (EuroSys), 2009.

Digital Library

[24]

J. R. Quinlan. C4.5: Programs for Machine Learning. 1993.

Digital Library

[25]

Red Hat / JBoss. JBoss Infinispan. http://www.jboss.org/infinispan.

[26]

Z. Shen, S. Subbiah, X. Gu, and J. Wilkes. Cloudscale: elastic resource scaling for multi-tenant cloud systems. In Proc. of the ACM Symposium on Cloud Computing (SOCC), 2011.

Digital Library

[27]

R. Singh, U. Sharma, E. Cecchet, and P. Shenoy. Autonomic mix-aware provisioning for non-stationary data center workloads. In Proc. of the International Conference on Autonomic Computing (ICAC), 2010.

Digital Library

[28]

Y. C. Tay, N. Goodman, and R. Suri. Locking performance in centralized databases. ACM Transactions on Database Systems, 10, 1985.

Digital Library

[29]

S. Thompson. Sampling. 2002.

[30]

L. Wang, J. Xu, M. Zhao, Y. Tu, and J. A. B. Fortes. Fuzzy modeling based resource management for virtualized database systems. In Proc. of the International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS), 2011.

Digital Library

[31]

Z. Wang, X. Zhu, and S. Singhal. Utilization and slo-based control for dynamic sizing of resource partitions. In Proc. of IFIP/IEEE Distributed Systems: Operations and Management (DSOM), 2005.

Digital Library

[32]

G. Welch and G. Bishop. An introduction to the kalman filter. Technical report, 1995.

Digital Library

[33]

P. Xiong, Y. Chi, S. Zhu, J. Tatemura, C. Pu, and H. HacigümüŞ. Activesla: a profit-oriented admission control framework for database-as-a-service providers. In Proc. of the ACM Symposium on Cloud Computing (SOCC), 2011.

Digital Library

[34]

P. S. Yu, D. M. Dias, and S. S. Lavenberg. On the analytical modeling of database concurrency control. Journal of the ACM (JACM), 40, 1993.

Digital Library

[35]

Transaction Processing Performance Council. TPC Benchmark™ C, Standard Specification, Revision 5.1. Transaction Processing Perfomance Council, 2002.

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
Show More Cited By

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

Recommendations

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

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 ...
On the analytical modeling of concurrency control algorithms for Software Transactional Memories: The case of Commit-Time-Locking

We present an analytical performance modeling approach for concurrency control algorithms in the context of Software Transactional Memories (STMs). We consider a realistic execution pattern where each thread alternates the execution of transactional and ...
Revisiting phased transactional memory
ICS '17: Proceedings of the International Conference on Supercomputing

In recent years, Hybrid TM (HyTM) has been proposed as a transactional memory approach that leverages on the advantages of both hardware (HTM) and software (STM) execution modes. HyTM assumes that concurrent transactions can have very different phases ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

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.

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]

Sponsors

University of Arizona: University of Arizona
SIGARCH: ACM Special Interest Group on Computer Architecture

In-Cooperation

IEEE

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 September 2012

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

ICAC '12

Sponsor:

University of Arizona
SIGARCH

ICAC '12: 9th International Conference on Autonomic Computing

September 18 - 20, 2012

California, San Jose, USA

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

30
Total Citations
View Citations
269
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 12 Nov 2024

Other Metrics

View Author Metrics

Citations

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
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents