skip to main content
10.1145/2811222.2811230acmconferencesArticle/Chapter ViewAbstractPublication PagescikmConference Proceedingsconference-collections
research-article

Eco-DMW: Eco-Design Methodology for Data warehouses

Published: 22 October 2015 Publication History

Abstract

In the Big Data Era, the management of energy consumption by servers and data centers has become a challenging issue for companies, institutions, and countries. In data-centric applications, DBMS are one of the major energy consumers when executing complex queries involving very large databases. Some research has been devoted to this issue, covering both the hardware and software dimensions. Regarding software, several proposals have been outlined, focusing either on analytical cost models to predict energy when executing queries or techniques to save energy. To this date, no research has taken account of energy at the physical design level, a crucial phase in database design. In this paper, we propose a methodology, called Eco-DMW, that integrates the energy dimension into the physical design. To show this integration, we study the case of materialized views, a redundant optimization structure. We first show the place that energy takes throughout this stage of design. A multi-objective formalization of the problem of materialized view selection is given. A genetic algorithm is developed to solve the problem. Intensive experiments are conducted using a mathematical cost model and a real measurement tool dedicated to computing energy. Results show the interest of this proposal to save energy and optimize queries in the presence of the selected materialized views.

References

[1]
R. Agrawal, A. Ailamaki, P. A. Bernstein, E. A. Brewer, M. J. Carey, et al. The claremont report on database research. ACM SIGMOD Record, 37(3):9--19, 2008.
[2]
S. Barielle. Calculating tco for energy. IBM Systems Magazine, http://www.ibmsystemsmag.com/mainframe/Business-Strategy/ROI/energy_estimating/, November 2011.
[3]
S. Borzsony, D. Kossmann, and K. Stocker. The skyline operator. In ICDE, pages 421--430, 2001.
[4]
S. Chaudhuri and V. R. Narasayya. Self-tuning database systems: A decade of progress. In VLDB, pages 3--14, 2007.
[5]
T. N. R. D. Council. Scaling up energy efficiency across the data center industry: Evaluating key drivers and barriers. Issue Paper, http://www.nrdc.org/energy/files/data-center-efficiency-assessment-IP.pdf, August 2014.
[6]
K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan. A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Transactions on Evolutionary Computation, 6(2):182--197, 2002.
[7]
I. Elghandour, A. Aboulnaga, D. C. Zilio, and C. Zuzarte. Recommending XML physical designs for XML databases. VLDB Journal, 22(4):447--470, 2013.
[8]
G. Graefe. Database servers tailored to improve energy efficiency. In Proceedings of the 2008 EDBT workshop on Software engineering for tailor-made data management, pages 24--28. ACM, 2008.
[9]
H. Gupta and I. S. Mumick. Selection of views to materialize under a maintenance cost constraint. In ICDT, pages 453--470. 1999.
[10]
S. Harizopoulos, M. Shah, J. Meza, and P. Ranganathan. Energy efficiency: The new holy grail of data management systems research. arXiv preprint arXiv:0909.1784, 2009.
[11]
M. Kunjir, P. K. Birwa, and J. R. Haritsa. Peak power plays in database engines. In EDBT, pages 444--455. ACM, 2012.
[12]
W. Lang, R. Kandhan, and J. M. Patel. Rethinking query processing for energy efficiency: Slowing down to win the race. IEEE Data Eng. Bull., 34(1):12--23, 2011.
[13]
W. Lang and J. Patel. Towards eco-friendly database management systems. arXiv preprint arXiv:0909.1767, 2009.
[14]
I. Mami and Z. Bellahsene. A survey of view selection methods. SIGMOD Record, 41(1):20--29, 2012.
[15]
J. C. McCullough, Y. Agarwal, J. Chandrashekar, S. Kuppuswamy, A. C. Snoeren, and R. K. Gupta. Evaluating the effectiveness of model-based power characterization. In USENIX Annual Technical Conf, 2011.
[16]
P. O'Neil, E. O'Neil, X. Chen, and S. Revilak. The star schema benchmark and augmented fact table indexing. In Performance evaluation and benchmarking, pages 237--252. Springer, 2009.
[17]
M. Poess and R. O. Nambiar. Energy cost, the key challenge of today's data centers: a power consumption analysis of tpc-c results. PVLDB, 1(2):1229--1240, 2008.
[18]
M. Rodriguez-Martinez, H. Valdivia, J. Seguel, and M. Greer. Estimating power/energy consumption in database servers. Procedia Computer Science, 6:112--117, 2011.
[19]
K. A. Ross, D. Srivastava, and S. Sudarshan. Materialized view maintenance and integrity constraint checking: Trading space for time. In ACM SIGMOD Record, volume 25, pages 447--458. ACM, 1996.
[20]
A. Roukh. Estimating power consumption of batch query workloads. To appear in MEDI 2015.
[21]
A. Roukh and L. Bellatreche. Eco-processing of olap complex queries. To appear in DaWaK 2015.
[22]
L. Siksnys, C. Thomsen, and T. B. Pedersen. MIRABEL DW: managing complex energy data in a smart grid. In DAWAK, pages 443--457, 2012.
[23]
Y.-C. Tu, X. Wang, B. Zeng, and Z. Xu. A system for energy-efficient data management. ACM SIGMOD Record, 43(1):21--26, 2014.
[24]
Z. Xu, Y.-C. Tu, and X. Wang. Exploring power-performance tradeoffs in database systems. In ICDE, pages 485--496, 2010.
[25]
Z. Xu, Y.-C. Tu, and X. Wang. Dynamic energy estimation of query plans in database systems. In ICDCS, pages 83--92. IEEE, 2013.
[26]
J. Yang, K. Karlapalem, and Q. Li. Algorithms for materialized view design in data warehousing environment. In VLDB, pages 25--29, 1997.
[27]
A. Zhou, B. Qu, H. Li, S. Zhao, P. N. Suganthan, and Q. Zhang. Multiobjective evolutionary algorithms: A survey of the state of the art. Swarm and Evolutionary Computation, Elsevier, 1(1):32--49, 2011.

Cited By

View all
  • (2021)Energy Efficiency vs. Performance of Analytical Queries: The case of Bitmap Join Indexes2021 IEEE International Conference on Big Data (Big Data)10.1109/BigData52589.2021.9671307(3066-3074)Online publication date: 15-Dec-2021
  • (2021)Capitalizing the database cost models process through a service‐based pipelineConcurrency and Computation: Practice and Experience10.1002/cpe.646335:11Online publication date: 11-Jul-2021
  • (2019)Energy efficiency optimization in big data processing platform by improving resources utilizationSustainable Computing: Informatics and Systems10.1016/j.suscom.2018.11.01121(80-89)Online publication date: Mar-2019
  • Show More Cited By

Index Terms

  1. Eco-DMW: Eco-Design Methodology for Data warehouses

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    DOLAP '15: Proceedings of the ACM Eighteenth International Workshop on Data Warehousing and OLAP
    October 2015
    108 pages
    ISBN:9781450337854
    DOI:10.1145/2811222
    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

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 22 October 2015

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. energy efficiency
    2. physical design
    3. power management

    Qualifiers

    • Research-article

    Conference

    CIKM'15
    Sponsor:

    Acceptance Rates

    DOLAP '15 Paper Acceptance Rate 8 of 31 submissions, 26%;
    Overall Acceptance Rate 29 of 79 submissions, 37%

    Upcoming Conference

    CIKM '25

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)3
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 10 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2021)Energy Efficiency vs. Performance of Analytical Queries: The case of Bitmap Join Indexes2021 IEEE International Conference on Big Data (Big Data)10.1109/BigData52589.2021.9671307(3066-3074)Online publication date: 15-Dec-2021
    • (2021)Capitalizing the database cost models process through a service‐based pipelineConcurrency and Computation: Practice and Experience10.1002/cpe.646335:11Online publication date: 11-Jul-2021
    • (2019)Energy efficiency optimization in big data processing platform by improving resources utilizationSustainable Computing: Informatics and Systems10.1016/j.suscom.2018.11.01121(80-89)Online publication date: Mar-2019
    • (2018)Data Stream Processing at Network Edges2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)10.1109/IPDPSW.2018.00106(657-665)Online publication date: May-2018
    • (2018)Parallel and Distributed Data WarehousesEncyclopedia of Database Systems10.1007/978-1-4614-8265-9_261(2644-2654)Online publication date: 7-Dec-2018
    • (2017)MetricStore repositoryProceedings of the Symposium on Applied Computing10.1145/3019612.3019821(1820-1825)Online publication date: 3-Apr-2017
    • (2017)Eco-Physic: Eco-Physical design initiative for very large databasesInformation Systems10.1016/j.is.2017.01.00368(44-63)Online publication date: Aug-2017
    • (2017)Towards an Explicitation and a Conceptualization of Cost Models in Database SystemsModel and Data Engineering10.1007/978-3-319-66854-3_17(223-231)Online publication date: 6-Sep-2017
    • (2017)Step by Step Towards Energy-Aware Data Warehouse DesignBusiness Intelligence10.1007/978-3-319-61164-8_5(105-138)Online publication date: 4-Jul-2017
    • (2017)Eco-Data Warehouse Design Through Logical VariabilitySOFSEM 2017: Theory and Practice of Computer Science10.1007/978-3-319-51963-0_34(436-449)Online publication date: 11-Jan-2017
    • Show More Cited By

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media