Intelligent Cloud Service Selection Using Agents

  • Imran Mujaddid Rabbani
  • Aslam Muhammad
  • Martinez Enriquez A.M.
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 209)

Abstract

One of the most recent developments within computer science is cloud computing which provides services (power, storage, platform, infrastructure etc.). Many clouds provide services are based on cost, efficiency, performance, and quality. Stakeholders have to compromise cost sometimes and performance or quality other times. Provision of the best quality based services to its stakeholders and to impart intelligence, agents can play important roles especially by learning the structure of the clouds. Agents can be trained to observe differences and behave intelligently for service selection. To rank different clouds, we propose a new technique performance factor for the provision of services based on intelligence. The research objective is to enable cloud users in selecting cloud service according to their own requirements. The technique assigns performance factor for each service provided by cloud and ranks it as whole. By doing so, quality of the services can be highly improved. We validate our approach with a case study, which emphasizes the need to rank cloud services of widely spreading and complex domains.

Keywords

Agents cloud computing performance factor 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
  2. 2.
    Don, G.: Intelligent Agents: The Right Information at the Right Time, IBM Corporation. Research Triangle Park, NC, USA (1997), http://www.networking.ibm.com/iag/iaghome.html
  3. 3.
    Gurmeet, S.: Scope of machine Learning in Cloud Computing (2010) Google Scholar
  4. 4.
    Armbrust, M., Fox, A., Grith, R., Joseph, A.D., Katz, R., Konwinski, A., Lee, H., Patterson, D., Rabkin, A., Stoica, I., Zaharia, M.: Above the Clouds: A Berkeley View of Cloud Computing. Technical Report No. UCB/EECS-2009-28, UC Berkeley Reliable Adaptive Distributed Systems Laboratory (2009)Google Scholar
  5. 5.
    Domenico, T.: Cloud Computing and Software Agents: Towards Cloud Intelligent Services, ICAR-CNR & University of Calabria Rende, ItalyGoogle Scholar
  6. 6.
    Myougnjin, K., Hanku, L., Hyogun, Y., Jee-In, K., HyungSeok, K.: IMAV: An Intelligent Multi-Agent Model Based on Cloud Computing for Resource Virtualization. In: 2011 International Conference on Information and Electronics Engineering, IPCSIT, vol. 6, pp. 199–203. IACSIT Press, Singapore (2011)Google Scholar
  7. 7.
    Shailesh, K.: Chandramohan: Personalized Web Service Selection. International Journal of Web & Semantic Technology (IJWesT) 2(2), 78–93 (2011)CrossRefGoogle Scholar
  8. 8.
    Kassidy, C., Martijn, W., Frances, M.T.: An Intelligent Cloud Resource Allocation Service. Agent-based automated Cloud resource allocation using micro-agreements. Faculty of Technology, Policy and Management, Delft University of Technology, Jaffalaan 5, Delft, The NetherlandsGoogle Scholar
  9. 9.
    Singh, A., Malhotra, M.: Agent Based Framework for Scalability in Cloud Computing. International Journal of Computer Science & Engineering Technology (IJCSET) 3(4), 41–45 (2012) ISSN: 2229-3345 Google Scholar
  10. 10.
    Sergiy, N., Vagan, T., Michal N.: Mastering Intelligent Clouds. Engineering Intelligent Data Processing Services in the Cloud. Industrial Ontologies Group, University of Jyväskylä, Mattilanniemi, Jyväskylä, FinlandGoogle Scholar
  11. 11.
    Höfer, C.N., Karagiannis, G.: Cloud computing services: taxonomy and comparison. Internet Serv. Appl. 2, 81–94 (2011) Google Scholar
  12. 12.
    White Paper : Introduction to Cloud Computing, by Dialogic Corporation (2012), www.dialogic.com
  13. 13.
    Network World 2012, Top Cloud Computing Companies List To Watch and Invest in 2012 (May 22, 2012), http://nanospeck.hubpages.com/hub/Best-Cloud-Service-Providers
  14. 14.
    Expert Group Report, The Future of Cloud Computing Opportunities For European Cloud Computing Beyond 2010, Public Version 1.0, By European Commission (2009)Google Scholar
  15. 15.
    Cloud Services Comparison (September 26, 2012), http://www.cloud-computing.findthebest.com
  16. 16.
    James J.: Using an Intelligent agents to enhancing search engine performance. First Monday 2(3) (1997) Google Scholar
  17. 17.
    Michael, E.M., Muninder, P.S.: Agent-based Architecture for Autonomic Web Service Selection. In: 1st International Workshop on Web Services and Agent Based Engineering. IBM Corporation and NCSU (2003)Google Scholar
  18. 18.
    Michael, E.M., Muninder, P.S.: Agent Based Trust Model Involving Multiple Qualities. In: 4th Int. Joint Conf. on Autonomous Agents and Multi-agent Systems. IBM Corporation and NCSU (2005)Google Scholar
  19. 19.
    Watkins, C.J.C.H.: Learning from delayed rewards. PhD Thesis, University of Cambridge, England (1989)Google Scholar
  20. 20.
    Rehman, Z.U., Hussain, F.K., Hussain, O.K.: Towards Multi-Criteria Cloud Service Selection. In: Fifth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, pp. 44–48. IEEE (2011)Google Scholar
  21. 21.
    Arkaitz, R., Marty, H.: An Automated Approach to Cloud Storage Service Selection. In: The Proceeding of Science Cloud 2011. ACM (2011), 978-1-4503-0699-7/11/06Google Scholar
  22. 22.
    Syed, A.Z., Aslam, M., Martinez-Enriquez, A.M.: Sentiment Analysis of Urdu Language: Handling Phrase-Level Negation. In: Batyrshin, I., Sidorov, G. (eds.) MICAI 2011, Part I. LNCS, vol. 7094, pp. 382–393. Springer, Heidelberg (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Imran Mujaddid Rabbani
    • 1
  • Aslam Muhammad
    • 1
  • Martinez Enriquez A.M.
    • 2
  1. 1.Department of CS & EUETLahorePakistan
  2. 2.Department of CSCINVESTAV-IPND.F. MexicoMexico

Personalised recommendations