Energy-Aware Design of Service-Based Applications

  • Alexandre Mello Ferreira
  • Kyriakos Kritikos
  • Barbara Pernici
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5900)


The continuous increase in electrical and computational power in data centers has been driving many research approaches under the Green IT main theme. However, most of this research focuses on reducing energy consumption considering hardware components and data center building features, like servers distribution and cooling flow. On the contrary, this paper points out that energy consumption is also a service quality problem, and presents an energy-aware design approach for building service-based applications. To this effect, techniques are provided to measure service costs combining Quality of Service (QoS) requirements and Green Performance Indicators (GPI) in order to obtain a better tradeoff between energy efficiency and performance for each user.


Execution Time Service Composition Global Constraint Composite Service Server Class 
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.


  1. 1. In Economic Downturn, Energy Efficiency and IT Take on Green Sheen (February 2009),
  2. 2.
    Tschudi, W.: Save Energy Now – Data Center Briefing. Technical report, Lawrence Berkeley National Laboratory (October 2008)Google Scholar
  3. 3.
    Schmidt, N.H., Erek, K., Kolbe, L.M., Zarnekow, R.: Towards a Procedural Model for Sustainable Information Systems Management. In: HICSS 2009: Proceedings of the 42nd Hawaii International Conference on System Sciences, Hawaii, USA, pp. 1–10. IEEE Computer Society, Los Alamitos (2009)Google Scholar
  4. 4.
    Bacon, D.F., Graham, S.L., Sharp, O.J.: Compiler Transformations for High-Performance Computing. ACM Computing Surveys 26(4), 345–420 (1994)CrossRefGoogle Scholar
  5. 5.
    Williams, J., Curtis, L.: Green: The New Computing Coat of Arms? IT Professional 10(1), 12–16 (2008)CrossRefGoogle Scholar
  6. 6.
    Zenker, N., Rajub, J.: Resource Measurement for Services in a heterogeneous Environment. In: ICTTA 2008: Proceedings of the 3rd International Conference on Information and Communication Technologies: From Theory to Applications, Damascus, Syria, IEEE Communications Society, pp. 1–15 (2008)Google Scholar
  7. 7.
    Barroso, L.A., Hölzle, U.: The Case for Energy-Proportional Computing. Computer 40(12), 33–37 (2007)CrossRefGoogle Scholar
  8. 8.
    Koomey, J.: Estimating total power consumption by servers in the U.S. and the world. Technical report, Analytics Press (February 2007),
  9. 9.
    Orgerie, A.C., Lefèvre, L., Gelas, J.P.: Save Watts in Your Grid: Green Strategies for Energy-Aware Framework in Large Scale Distributed Systems. In: ICPADS 2008: Proceedings of the 2008 14th IEEE International Conference on Parallel and Distributed Systems, Melbourne, Victoria, Australia, pp. 171–178. IEEE Computer Society, Los Alamitos (2008)CrossRefGoogle Scholar
  10. 10.
    U.S. Environmental Protection Agency (EPA): Report to Congress on Server and Data Center Energy Efficiency – Public Law 109-431. Technical report (August 2007)Google Scholar
  11. 11.
    Wang, D.: Meeting Green Computing Challenges. In: HDP 2007: Proceedings of the International Symposium on High Density packaging and Microsystem Integration, Shanghai, China, pp. 1–4. IEEE Computer Society, Los Alamitos (2007)Google Scholar
  12. 12.
    Xue, J.W.J., Chester, A.P., He, L.G., Jarvis, S.A.: Model-driven Server Allocation in Distributed Enterprise Systems. In: ABIS 2009: Proceedings of the 3rd International Conference on Adaptive Business Information Systems, Leipzig, Germany (March 2009)Google Scholar
  13. 13.
    Liu, L., Wang, H., Liu, X., Jin, X., He, W.B., Wang, Q.B., Chen, Y.: GreenCloud: a new architecture for green data center. In: ICAC-INDST 2009: Proceedings of the 6th international conference industry session on Autonomic computing and communications industry session, Barcelona, Spain, pp. 29–38. ACM, New York (2009)CrossRefGoogle Scholar
  14. 14.
    Comuzzi, M., Pernici, B.: A Framework for QoS-Based Web Service Contracting. ACM Transactions on the Web (June 2009)Google Scholar
  15. 15.
    Plebani, P., Pernici, B.: URBE: Web Service Retrieval Based on Similarity Evaluation. IEEE Transactions on Knowledge and Data Engineering (2009)Google Scholar
  16. 16.
    Zeng, L., Benatallah, B., Ngu, A.H., Dumas, M., Kalagnanam, J., Chang, H.: QoS-Aware Middleware for Web Services Composition. IEEE Transactions on Software Engineering 30(5), 311–327 (2004)CrossRefGoogle Scholar
  17. 17.
    Ardagna, D., Pernici, B.: Adaptive Service Composition in Flexible Processes. IEEE Transactions on Software Engineering 3(6), 369–384 (2007)CrossRefGoogle Scholar
  18. 18.
    Canfora, G., Di Penta, M., Esposito, R., Villani, M.L.: QoS-Aware Replanning of Composite Web Services. In: ICWS 2005: Proceedings of the IEEE International Conference on Web Services, Orlando, FL, USA, pp. 121–129. IEEE Computer Society, Los Alamitos (2005)Google Scholar
  19. 19.
    Jaeger, M.C., Mühl, G., Golze, S.: QoS-Aware Composition of Web Services: A Look at Selection Algorithms. In: ICWS 2005: IEEE International Conference on Web Services, Orlando, FL, USA, pp. 807–808. IEEE Computer Society, Los Alamitos (2005)CrossRefGoogle Scholar
  20. 20.
    Rossi, F., van Beek, P., Walsh, T.: Handbook of Constraint Programming. Elsevier Science Inc., New York (2006)zbMATHGoogle Scholar
  21. 21.
    Hwang, C., Yoon, K.: Multiple Criteria Decision Making. LNEMS (1981)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Alexandre Mello Ferreira
    • 1
  • Kyriakos Kritikos
    • 1
  • Barbara Pernici
    • 1
  1. 1.Dipartimento di Elettronica e InformazionePolitecnico di MilanoItaly

Personalised recommendations