Open Access Open Access  Restricted Access Subscription or Fee Access

Query Processing and Energy Consumptions in Data Centers

Vijaya Kumar S, T.K.P. Rajagopal, Seema Pillai

Abstract


Many researches are being conducted now in the field of software and hardware on energy consumption. Now a days it’s a huge challenge in front of us to handle the energy consumed for the large data centers which ideally deal with the large data transactions and storage. In the datacenters, on a daily basis many large queries are being run and a large amount of data is getting transacted, monitored and controlled. Usually all these actions are done with huge database systems which support efficient processing plans. In this paper, alternative methods to define the energy consumption plans for the database queries have been presented.

Cite this Article
Vijaya Kumar S, Rajagopal TKP, Seema Pillai. Query Processing and Energy Consumptions in Data Centers. Journal of Advanced Database Management & Systems. 2016; 3(2): 9–16p.


Keywords


Data, data centers, database systems, query execution plans, energy consumption plans

Full Text:

PDF

References


Muralikrishna M, Dewitt DJ. Equi-Depth Histograms for Estimating Selectivity Factors for Multi-Dimensional Queries. Proc. of ACM SIGMOD, Chicago. 1988.

Xu Z, Tu Y, Wang X. Exploring Power-Performance Tradeoffs in Database Systems. In ICDE. 2010; 485–496p.

Connolly TM, Begg CE. Database Systems: A Practical Approach to Design, Implementation and Management. 5th Edn. Pearson Addison Wesley; 2010.

Garcia-Molina H, Ullman JD, Widom J. Database Systems: The Complete Book. 2nd Edn. Upper Saddle River, NJ, USA: Prentice Hall Press; 2008.

Mackert LF, Lohman GM. Index Scans Using a Finite LRU Buffer: A Validated I/O Model. Transactions on Database Systems (TODS). 1989; 14(3): 401–424p.

Watts Up? Watts Up? Plug Load Meters. [online] Available at: [Accessed 20 Jul 2014].

Chaudhuri S. An Overview of Query Optimization in Relational Systems. In Proceedings of the Seventeenth ACM SIGACTSIGMOD-SIGART Symposium on Principles of Database Systems, PODS ’98. 1998; 34–43p.

Kumar R. Data Center Power, Cooling and Space: A Worrisome Outlook for the Next Two Years. 2010. Available at: https://www.gartner.com/doc/1313015/data-center-power-coolingspace [Accessed 15 Oct 2014].

Ono K, Lohman GM. Measuring the Complexity of Join Enumeration in Query Optimization. In Proc. of VLDB, Brisbane. 1990.

Ozsu MT, Valduriez P. Principles of Distributed Database Systems. Prentice-Hall; 1991.

Piatetsky-Shapiro G, Connell C. Accurate Estimation of the Number of Tuples Satisfying a Condition. In Proc. of ACMSIGMOD. 1984.

Pirahesh H, Hellerstein JM, Hasan W. Extensible/Rule Based Query Rewrite Optimization in Starburst. In Proc. of ACMSIGMOD. 1992.

Poosala V, Ioannidis Y, Haas P, et al. Improved Histograms for Selectivity Estimation. In Proc. of ACM SIGMOD, Montreal, Canada. 1996.

Seshadri P, et al. Cost Based Optimization for Magic: Algebra and Implementation. In Proc. of ACM SIGMOD, Montreal. 1996.

Seshadri P, Pirahesh H, Leung TYC. Decorrelating Complex Queries. In Proc. of the IEEE International Conference on Data Engineering. 1996.

Simmen D, Shekita E, Malkemus T. Fundamental Techniques for Order Optimization. In Proc. of ACM SIGMOD, Montreal. 1996.

Srivastava D, Dar S, Jagadish HV, Levy A. Answering Queries with Aggregation Using Views. Proc. of VLDB, Mumbai. 1996.

Yan YP, Larson PA. Eager Aggregation and Lazy Aggregation. In Proc. of VLDB Conference, Zurich. 1995.

Yang HZ, Larson PA. Query Transformation for PSJ-Queries. In Proc. of VLDB. 1987.


Refbacks

  • There are currently no refbacks.


This site has been shifted to https://stmcomputers.stmjournals.com/