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Modeling the Offloading of Different Types of Mobile Applications by Using Evolutionary Algorithms

  • Gianluigi FolinoEmail author
  • Francesco S. Pisani
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8602)

Abstract

Modern smartphones permit to run a large variety of applications, i.e. multimedia, games, social network applications, etc. However, this aspect considerably reduces the battery life of these devices. A possible solution to alleviate this problem is to offload part of the application or the whole computation to remote servers, i.e. Cloud Computing. The offloading cannot be performed without considering the issues derived from the nature of the application (i.e. multimedia, games, etc.), which can considerably change the resources necessary to the computation and the type, the frequency and the amount of data to be exchanged with the network. This work shows a framework for automatically building models for the offloading of mobile applications based on evolutionary algorithms and how it can be used to simulate different kinds of mobile applications and to analyze the rules generated. To this aim, a tool for generating mobile datasets, presenting different features, is designed and experiments are performed in different usage conditions in order to demonstrate the utility of the overall framework.

Keywords

Execution Time Mobile Device Cloud Computing Cloud Server Mobile Application 
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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References

  1. 1.
    Buyya, R., Yeo, C.S., Venugopal, S., Broberg, J., Brandic, I.: Cloud computing and emerging it platforms: Vision, hype, and reality for delivering computing as the 5th utility. Future Generation Computer Systems 25(6), 599–616 (2009)Google Scholar
  2. 2.
    Folino, G., Pizzuti, C., Spezzano, G.: Gp ensembles for large-scale data classification. IEEE Transactions on Evolutionary Computation 10(5), 604–616 (2006)CrossRefGoogle Scholar
  3. 3.
    Folino, G., Pisani, F.S.: A Framework for Modeling Automatic Offloading of Mobile Applications Using Genetic Programming. In: Esparcia-Alcázar, A.I. (ed.) EvoApplications 2013. LNCS, vol. 7835, pp. 62–71. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  4. 4.
    Gurun, S., Krintz, C.: Addressing the energy crisis in mobile computing with developing power aware software. In UCSB Technical Report, UCSB Computer Science Department (2003)Google Scholar
  5. 5.
    Kliazovich, D., Bouvry, P., Audzevich, Y., Khan, S.U.: Greencloud: A packet-level simulator of energy-aware cloud computing data centers. In: Proceedings of the Global Communications Conference, GLOBECOM 2010, pp. 1–5. IEEE, Miami (2010)Google Scholar
  6. 6.
    Kumar, K., Yung-Hsiang, L.: Cloud computing for mobile users: Can offloading computation save energy? IEEE Computer 43(4), 51–56 (2010)CrossRefGoogle Scholar
  7. 7.
    Lee, K., Rhee, I., Lee, J., Chong, S., Yi, Y.: Mobile data offloading: how much can wifi deliver? IEEE/ACM Transactions on Networking 21(2), 536–550 (2013)CrossRefGoogle Scholar
  8. 8.
    Liu, J., Kumar, K., Lu, Y-H.: Tradeoff between energy savings and privacy protection in computation offloading. In: Proceedings of the 2010 International Symposium on Low Power Electronics and Design, pp. 213–218. ACM, Austin (2010)Google Scholar
  9. 9.
    Wolski, R., Gurun, S., Krintz, C., Nurmi, D.: Using bandwidth data to make computation offloading decisions. In: 22nd IEEE International Symposium on Parallel and Distributed Processing, IPDPS 2008, pp. 1–8. IEEE, Miami (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Institute of High Performance Computing and Networking (ICAR-CNR)RendeItaly

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