Energy Aware Database Management

  • Christian Bunse
  • Hagen Höpfner
  • Sonja Klingert
  • Essam Mansour
  • Suman Roychoudhury
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8343)


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.


Energy Consumption Cloud Service Cloud Provider Energy Complexity Input Size 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 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)CrossRefGoogle Scholar
  2. 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)CrossRefGoogle Scholar
  3. Brejová, B.: Analyzing variants of Shellsort. Information Processing Letters 79(5), 223–227 (2001)CrossRefzbMATHMathSciNetGoogle Scholar
  4. 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)Google Scholar
  5. 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)Google Scholar
  6. Bunse, C., Klingert, S., Schulze, T.: GreenSLAs for the Energy-efficient Management of Data Centres. In: E-Energy 2011 Proc. (2011)Google Scholar
  7. 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)CrossRefGoogle Scholar
  8. 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)CrossRefGoogle Scholar
  9. 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)CrossRefGoogle Scholar
  10. 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)CrossRefzbMATHGoogle Scholar
  11. 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)CrossRefGoogle Scholar
  12. Hoare, C.A.R.: Quicksort. Computer Journal 5(1), 10–15 (1962)CrossRefzbMATHMathSciNetGoogle Scholar
  13. Höpfner, H., Bunse, C.: Energy Aware Data Management on AVR Micro Controller Based Systems. ACM SIGSOFT SEN 35(3) (2010a)Google Scholar
  14. 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. (2010b)Google Scholar
  15. 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)Google Scholar
  16. 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)Google Scholar
  17. Lafond, S., Lilius, J.: Energy consumption analysis for two embedded Java virtual machines. Journal of Systems Architecture 53(5-6), 328–337 (2007)CrossRefGoogle Scholar
  18. Lafore, R.: Data Structures and Algorithms in Java, 2nd edn. SAMS Publishing, Indianapolis (2002)Google Scholar
  19. Lancaster, D.E.: TTL Cookbook. Sams (1974)Google Scholar
  20. 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)CrossRefGoogle Scholar
  21. 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)Google Scholar
  22. 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)Google Scholar
  23. 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)Google Scholar
  24. 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)Google Scholar
  25. 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)Google Scholar
  26. 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)CrossRefGoogle Scholar
  27. 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)CrossRefGoogle Scholar
  28. 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)CrossRefGoogle Scholar
  29. 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)Google Scholar
  30. 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)Google Scholar
  31. 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)CrossRefGoogle Scholar
  32. 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)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Christian Bunse
    • 1
  • Hagen Höpfner
    • 1
  • Sonja Klingert
    • 1
  • Essam Mansour
    • 1
  • Suman Roychoudhury
    • 1
  1. 1.Fachhochschule Stralsund, Bauhaus-Universität Weimar, Universität Mannheim, King Abdullah University, Tata ConsultingGermany

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