Abstract
Data center and cloud providers are responsible for providing services such as storage or retrieval for large amounts of (customer owned) data by using databsae management systems (DBMS). Service provision implies a specific quality of service regarding performance or security. Another factor of increasing importance is energy consumption. Although not a top priority for most customers, the cost of energy and thus (indirectly) the cost of service provision is key for both, customer and provider. Typically, energy consumption is viewed as a hardware related issue. Only recently, research has proved that software has a significant impact onto the energy consumption of a system too. Database management systems comprise various algorithms for efficiently retrieving and managing data. Typically, algorithm efficiency or performance is correlated with execution speed. This paper reports our results concerning the energy consumption of different implementations of sorting and join algorithms. We demonstrate that high performance algorithms often require more energy than slower ones. Furthermore, we show that dynamically exchanging algorithms at runtime results in a better throughput.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Badea, C., Nicolau, A., Veidenbaum, A.V.: Impact of JVM superoperators on energy consumption in resource-constrained embedded systems. ACM SIGPLAN Notices 43(7), 23–30 (2008)
Bardine, A., Foglia, P., Gabrielli, G., Prete, C.A.: Analysis of static and dynamic energy consumption in NUCA caches: Initial results. In: Proceedings of the 2007 Workshop on MEmory Performance: DEaling with Applications, Systems and Architecture, pp. 105–112. ACM, New York (2007)
Brejová, B.: Analyzing variants of Shellsort. Information Processing Letters 79(5), 223–227 (2001)
Bunse, C., Höpfner, H.: Ocemes: Measuring overall and component-based energy demands of mobile and embedded systems. In: Goltz, U., Magnor, M.A., Appelrath, H.-J., Matthies, H.K., Balke, W.-T., Wolf, L.C. (eds.) GI-Jahrestagung. LNI, vol. 208, pp. 434–440. GI (2012)
Bunse, C., Höpfner, H., Mansour, E., Roychoudhury, S.: Exploring the Energy Consumption of Data Sorting Algorithms in Embedded and Mobile Environments. In: ROSOC-M 2009 Proceedings (2009) (forthcoming)
Bunse, C., Klingert, S., Schulze, T.: GreenSLAs for the Energy-efficient Management of Data Centres. In: E-Energy 2011 Proc. (2011)
Bunse, C., Klingert, S., Schulze, T.: Greenslas: Supporting energy-efficiency through contracts. In: Huusko, J., de Meer, H., Klingert, S., Somov, A. (eds.) E2DC 2012. LNCS, vol. 7396, pp. 54–68. Springer, Heidelberg (2012)
Chen, J.-J., Thiele, L.: Expected system energy consumption minimization in leakage-aware DVS systems. In: ISLPED 2008: Proceeding of the Thirteenth International Symposium on Low Power Electronics and Design, pp. 315–320. ACM, New York (2008)
Farkas, K.I., Flinn, J., Back, G., Grunwald, D., Anderson, J.M.: Quantifying the energy consumption of a pocket computer and a Java virtual machine. In: SIGMETRICS 2000: Proceedings of the 2000 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, pp. 252–263. ACM, New York (2000)
Feeney, L.M.: An Energy Consumption Model for Performance Analysis of Routing Protocols for Mobile Ad Hoc Networks. Mobile Networks and Applications 6(3), 239–249 (2001)
Gurun, S., Nagpurkar, P., Zhao, B.Y.: Energy consumption and conservation in mobile peer-to-peer systems. In: MobiShare 2006: Proceedings of the 1st International Workshop on Decentralized Resource Sharing in Mobile Computing and Networking, pp. 18–23. ACM, New York (2006)
Hoare, C.A.R.: Quicksort. Computer Journal 5(1), 10–15 (1962)
Höpfner, H., Bunse, C.: Energy Aware Data Management on AVR Micro Controller Based Systems. ACM SIGSOFT SEN 35(3) (2010a)
Höpfner, H., Bunse, C.: Towards an energy-consumption based complexity classification for resource substitution strategies. In: Balke, W.-T., Lofi, C. (eds.) Grundlagen von Datenbanken. CEUR Workshop Proceedings, vol. 581. CEUR-WS.org (2010b)
Jain, R., Molnar, D., Ramzan, Z.: Towards understanding algorithmic factors affecting energy consumption: Switching complexity, randomness, and preliminary experiments. In: Workshop on Discrete Algothrithms and Methods for MOBILE Computing and Communications — Proceedings of the 2005 Joint Workshop on Foundations of Mobile Computing, pp. 70–79. ACM, New York (2005)
Koc, H., Ozturk, O., Kandemir, M., Narayanan, S.H.K., Ercanli, E.: Minimizing energy consumption of banked memories using data recomputation. In: ISLPED 2006: Proceedings of the 2006 International Symposium on Low Power Electronics and Design, pp. 358–362. ACM, New York (2006)
Lafond, S., Lilius, J.: Energy consumption analysis for two embedded Java virtual machines. Journal of Systems Architecture 53(5-6), 328–337 (2007)
Lafore, R.: Data Structures and Algorithms in Java, 2nd edn. SAMS Publishing, Indianapolis (2002)
Lancaster, D.E.: TTL Cookbook. Sams (1974)
Liveris, N., Zhou, H., Banerjee, P.: A dynamic-programming algorithm for reducing the energy consumption of pipelined system-level streaming applications. In: ASP-DAC 2008: Proceedings of the 2008 Conference on Asia and South Pacific Design Automation, pp. 42–48. IEEE Computer Society Press, Los Alamitos (2008)
Ozturk, O., Kandemir, M.: Nonuniform Banking for Reducing Memory Energy Consumption. In: DATE 2005: Proceedings of the Conference on Design, Automation and Test in Europe, pp. 814–819. IEEE Computer Society, Washington, DC (2005)
Potlapally, N.R., Ravi, S., Raghunathan, A., Jha, N.K.: A Study of the Energy Consumption Characteristics of Cryptographic Algorithms and Security Protocols. IEEE Transactions on Mobile Computing 5(2), 128–143 (2006)
Seddik-Ghaleb, A., Ghamri-Doudane, Y., Senouci, S.-M.: A performance study of TCP variants in terms of energy consumption and average goodput within a static ad hoc environment. In: IWCMC 2006: Proceedings of the 2006 International Conference on Wireless Communications and Mobile Computing, pp. 503–508. ACM, New York (2006)
Senouci, S.-M., Naimi, M.: New routing for balanced energy consumption in mobile ad hoc networks. In: PE-WASUN 2005: Proceedings of the 2nd ACM International Workshop on Performance Evaluation of Wireless Ad Hoc, Sensor, and Ubiquitous Networks, pp. 238–241. ACM, New York (2005)
Seo, C., Malek, S., Medvidovic, N.: An energy consumption framework for distributed java-based systems. In: ASE 2007: Proceedings of the Twenty-Second IEEE/ACM International Conference on Automated Software Engineering, pp. 421–424. ACM, New York (2007)
Shen, H., Kumar, M., Das, S.K., Wang, Z.: Energy-efficient data caching and prefetching for mobile devices based on utility. Mobile Networks and Application 10(4), 475–486 (2005)
Singh, H., Singh, S.: Energy consumption of tcp reno, newreno, and sack in multi-hop wireless networks. ACM SIGMETRICS Performance Evaluation Review 30(1), 206–216 (2002)
Sun, B., Gao, S.-X., Chi, R., Huang, F.: Algorithms for balancing energy consumption in wireless sensor networks. In: FOWANC 2008: Proceeding of the 1st ACM International Workshop on Foundations of Wireless Ad Hoc and Sensor Networking and Computing, pp. 53–60. ACM, New York (2008)
Tuan, T., Kao, S., Rahman, A., Das, S., Trimberger, S.: A 90nm low-power FPGA for battery-powered applications. In: FPGA 2006: Proceedings of the 2006 ACM/SIGDA 14th International Symposium on Field Programmable Gate Arrays, pp. 3–11. ACM, New York (2006)
Wang, L., French, M., Davoodi, A., Agarwal, D.: FPGA dynamic power minimization through placement and routing constraints. EURASIP Journal on Embedded Systems 2006(1) (2006)
Wick, M.R., Phillips, A.T.: Comparing the template method and strategy design patterns in a genetic algorithm application. SIGCSE Bull. 34(4), 76–80 (2002)
Zhang, M., Chang, X., Zhang, G.: Reducing cache energy consumption by tag encoding in embedded processors. In: ISLPED 2007: Proceedings of the 2007 International Symposium on Low Power Electronics and Design, pp. 367–370. ACM, New York (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bunse, C., Höpfner, H., Klingert, S., Mansour, E., Roychoudhury, S. (2014). Energy Aware Database Management. In: Klingert, S., Hesselbach-Serra, X., Ortega, M.P., Giuliani, G. (eds) Energy-Efficient Data Centers. Lecture Notes in Computer Science, vol 8343. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55149-9_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-55149-9_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-55148-2
Online ISBN: 978-3-642-55149-9
eBook Packages: Computer ScienceComputer Science (R0)