Energy Efficient Data Sorting Using Standard Sorting Algorithms

  • Christian Bunse
  • Hagen Höpfner
  • Suman Roychoudhury
  • Essam Mansour
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 50)


Protecting the environment by saving energy and thus reducing carbon dioxide emissions is one of today’s hottest and most challenging topics. Although the perspective for reducing energy consumption, from ecological and business perspectives is clear, from a technological point of view, the realization especially for mobile systems still falls behind expectations. Novel strategies that allow (software) systems to dynamically adapt themselves at runtime can be effectively used to reduce energy consumption. This paper presents a case study that examines the impact of using an energy management component that dynamically selects and applies the “optimal” sorting algorithm, from an energy perspective, during multi-party mobile communication. Interestingly, the results indicate that algorithmic performance is not key and that dynamically switching algorithms at runtime does have a significant impact on energy consumption.


Energy awareness Software engineering Adaptivity Mobile information systems 


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  1. 1.
    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
  2. 2.
    Brejová, B.: Analyzing variants of Shellsort. Information Processing Letters 79(5), 223–227 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Bunse, C., Höpfner, H.: Resource substitution with components — Optimizing Energy Consumption. In: Cordeiro, J., Shishkov, B., Ranchordas, A.K., Helfert, M. (eds.) Proceedings of the 3rd International Conference on Software and Data Technologie, vol. SE/GSDCA/MUSE, pp. 28–35. INSTICC press, Setúbal (2008)Google Scholar
  4. 4.
    Bunse, C., Höpfner, H., Mansour, E., Roychoudhury, S.: Exploring the Energy Consumption of Data Sorting Algorithms in Embedded and Mobile Environments. In: Proceedings of the 10th International Conference on Mobile Data Management: Systems, Services and Middleware, Taipei, Taiwan, May 18-20, pp. 600–607. IEEE Computer Society Press, Los Alamitos (2009)Google Scholar
  5. 5.
    Bunse, C., Höpfner, H., Roychoudhury, S., Mansour, E.: Choosing the “best” Sorting Algorithm for Optimal Energy Consumption. In: Proceedings of the 4th International Conference on Software and Data Technologie (ICSOFT 2009), Setúbal, Portugal, July 26-29, vol. 2, pp. 199–206. INSTICC press, Setúbal (2009)Google Scholar
  6. 6.
    Chen, J.-J., Thiele, L.: Expected system energy consumption minimization in leakage-aware DVS systems. In: Proceeding of the Thirteenth International Symposium on Low Power Electronics and Design, ISLPED 2008, pp. 315–320. ACM, New York (2008)CrossRefGoogle Scholar
  7. 7.
    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
  8. 8.
    Gamma, E., Helm, R., Johnson, R., Vlissides, J.: Design Patterns: Elements of Reusable Object-Oriented Software. Addison-Wesley Professional, Reading (1995)zbMATHGoogle Scholar
  9. 9.
    Gurun, S., Nagpurkar, P., Zhao, B.Y.: Energy consumption and conservation in mobile peer-to-peer systems. In: Proceedings of the 1st International Workshop on Decentralized Resource Sharing in Mobile Computing and Networking, MobiShare 2006, pp. 18–23. ACM, New York (2006)CrossRefGoogle Scholar
  10. 10.
    Hoare, C.A.R.: Quicksort. Computer Journal 5(1), 10–15 (1962)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Höpfner, H., Bunse, C.: Resource Substitution for the Realization of Mobile Information Systems. In: Filipe, J., Helfert, M., Shishkov, B. (eds.) Proceedings of the 2nd International Conference on Software and Data Technologie, vol. Software Engineering, pp. 283–289. INSTICC Press, Setúbal (2007)Google Scholar
  12. 12.
    Höpfner, H., Bunse, C.: Energy Aware Data Management on AVR Micro Controller Based Systems. ACM SIGSOFT Software Engineering Notes 35(3) (May 2010)Google Scholar
  13. 13.
    Höpfner, H., Bunse, C.: Towards an energy-consumption based complexity classification for resource substitution strategies. In: Balke, W.T., Lofi, C. (eds.) Proceedings of the 22. Workshop on Foundations of Databases (Grundlagen von Datenbanken), Bad Helmstedt, Germany, May 25-28. CEUR Workshop Proceeding, vol. 581 (2010),
  14. 14.
    Jain, R., Molnar, D., Ramzan, Z.: Towards understanding algorithmic factors affecting energy consumption: switching complexity, randomness, and preliminary experiments. In: Proceedings of the 2005 Joint Workshop on Foundations of Mobile Computing, Workshop on Discrete Algothrithms and Methods for MOBILE Computing and Communications, pp. 70–79. ACM, New York (2005)Google Scholar
  15. 15.
    Koc, H., Ozturk, O., Kandemir, M., Narayanan, S.H.K., Ercanli, E.: Minimizing energy consumption of banked memories using data recomputation. In: Proceedings of the 2006 International Symposium on Low Power Electronics and Design, ISLPED 2006, pp. 358–362. ACM, New York (2006)Google Scholar
  16. 16.
    Lafore, R.: Data Structures and Algorithms in Java, 2nd edn. SAMS Publishing, Indianapolis (2002)Google Scholar
  17. 17.
    Liveris, N., Zhou, H., Banerjee, P.: A dynamic-programming algorithm for reducing the energy consumption of pipelined system-level streaming applications. In: Proceedings of the 2008 Conference on Asia and South Pacific Design Automation, ASP-DAC 2008, Seoul, Korea, pp. 42–48. IEEE Computer Society Press, Los Alamitos (2008)Google Scholar
  18. 18.
    Ozturk, O., Kandemir, M.: Nonuniform Banking for Reducing Memory Energy Consumption. In: Proceedings of the Conference on Design, Automation and Test in Europe, DATE 2005, pp. 814–819. IEEE Computer Society Press, Washington (2005)Google Scholar
  19. 19.
    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)CrossRefGoogle Scholar
  20. 20.
    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: Proceedings of the 2006 International Conference on Wireless Communications and Mobile Computing, IWCMC 2006, pp. 503–508. ACM, New York (2006)Google Scholar
  21. 21.
    Senouci, S.-M., Naimi, M.: New routing for balanced energy consumption in mobile ad hoc networks. In: Proceedings of the 2nd ACM International Workshop on Performance Evaluation of Wireless ad hoc, Sensor, and Ubiquitous Networks, PE-WASUN 2005, Montreal, Quebec, Canada, pp. 238–241. ACM Press, New York (2005)Google Scholar
  22. 22.
    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
  23. 23.
    Sousa, J.P., Balan, R.K., Poladian, V., Garalan, D., Satyanarayanan, M.: User Guidance of Resource-Adaptive Systems. In: Cordeiro, J., Shishkov, B., Ranchordas, A., Helfert, M. (eds.) Proceedings of the Third International Conference on Software and Data Technologies, ICSOFT 2008, vol. SE/MUSE/GSDCA, pp. 36–45. INSTICC Press, Setúbal (2008)Google Scholar
  24. 24.
    Sun, B., Gao, S.-X., Chi, R., Huang, F.: Algorithms for balancing energy consumption in wireless sensor networks. In: Proceeding of the 1st ACM International Workshop on Foundations of Wireless ad hoc and Sensor Networking and Computing, FOWANC 2008, pp. 53–60. ACM, New York (2008)CrossRefGoogle Scholar
  25. 25.
    Tuan, T., Kao, S., Rahman, A., Das, S., Trimberger, S.: A 90nm low-power FPGA for battery-powered applications. In: Proceedings of the 2006 ACM/SIGDA 14th International Symposium on Field Programmable Gate Arrays, FPGA 2006, pp. 3–11. ACM, New York (2006)Google Scholar
  26. 26.
    Wang, L., French, M., Davoodi, A., Agarwal, D.: FPGA dynamic power minimization through placement and routing constraints. EURASIP Journal on Embedded Systems (1), 7 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Christian Bunse
    • 1
  • Hagen Höpfner
    • 2
  • Suman Roychoudhury
    • 3
  • Essam Mansour
    • 4
  1. 1.University of Applied Sciences StralsundStralsundGermany
  2. 2.Bauhaus University of WeimarWeimarGermany
  3. 3.Tata Research Development and Design CenterPuneIndia
  4. 4.King Abdullah University of Science and TechnologyThuwalKingdom of Saudi Arabia

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