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Is Cloud Self-organization Feasible?

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Adaptive Resource Management and Scheduling for Cloud Computing (ARMS-CC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9438))

Abstract

In this paper we discuss why cloud self-organization is not only desirable, but also critical for the future of cloud computing. We analyze major challenges and discuss practical principles for cloud self-organization. After a brief presentation of a hierarchical cloud architecture model we outline the advantages of a self-organization model based on coalition formation and combinatorial auctions.

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Notes

  1. 1.

    In 1961, in a speech given to celebrate MIT’s centennial, he suggested that in the future computing power and applications could be sold through the utility business model.

  2. 2.

    See for example the November 6 memorandum “The DoD Cloud Way Forward” which stresses the need for DoD to increase its use of cloud services.

  3. 3.

    The relation between cause and effect is often unpredictable: small causes could have large effects, and large causes could have small effects. This phenomena is caused by feedback, the results of an action or transformation are fed back and affect the system behavior.

  4. 4.

    Emergence is generally understood as a property of a system that is not predictable from the properties of individual system components.

References

  1. Ausubel, L., Cramton, P., Milgrom, P.: The clock-proxy auction: a practical combinatorial auction design. In: Cramton, P., Shoham, Y., Steinberg, R. (eds.) Combinatorial Auctions. MIT Press, Cambridge (2006)

    Google Scholar 

  2. Barossso, L.A., Clidaras, J., Hözle, U.: The Datacenter as a Computer; an Introduction to the Design of Warehouse-Scale Machines. Morgan & Claypool, San Rafael (2013)

    Google Scholar 

  3. Blackburn, M., Hawkins, A.: Unused server survey results analysis. http://www.thegreengrid.org/media/WhitePapers/Unused20Server20Study_WP_101910_v1.ashx?lang=en. Accessed 6 December 2013

  4. Bradic, I.: Towards self-manageable cloud services. In: Proceedings of the 33 International Conference on Computer Software and Applications, pp. 128–133 (2009)

    Google Scholar 

  5. Chang, V., Wills, G., De Roure, D.: A review of cloud business models and sustainability. In: Proceedings of the IEEE 3rd International Conference on Cloud Computing, pp. 43–50 (2010)

    Google Scholar 

  6. Gell-Mann, M.: Simplicity and complexity in the description of nature. Eng. Sci. Caltech LI(3), 3–9 (1988)

    Google Scholar 

  7. Ganek, A.G., Corbi, T.A.: The dawning of the autonomic computing era. IBM Syst. J. 42(1), 5–18 (2003). https://www.cs.drexel.edu/jsalvage/Winter2010/CS576/autonomic.pdf

    Article  Google Scholar 

  8. Jackson, K.R., Ramakrishnan, L., Muriki, K., Canon, S., Cholia, S., Shalf, J., Wasserman, H., Wright, N.J.: Performance analysis of high performance computing applications on the Amazon Web services cloud. In: Proceedings of IEEE Second International Confernce on Cloud Computing Technology and Science, pp. 159–168 (2010)

    Google Scholar 

  9. Kephart, J.O., Chase, D.M.: The vision of autonomic computing. Computer 36(1), 41–50 (2003)

    Article  MathSciNet  Google Scholar 

  10. Li, C., Sycara, K.: Algorithm for combinatorial coalition formation and payoff division in an electronic marketplace. In: Proceedings of AAMAS 2002 - First Joint International Conference on Autonomous Agents and Multiagent Systems, pp. 120–127 (2002)

    Google Scholar 

  11. Lim, H.C., Babu, S., Chase, J.S., Parekh, S.S.: Automated control in cloud computing: challenges and opportunities. In: Proceedings of the First Workshop on Automated Control for Datacenters and Clouds, pp. 13–18. ACM Press (2009)

    Google Scholar 

  12. Litoiu, M., Woodside, M., Wong, J., Ng, J., Iszlai, G.: Business driven cloud optimization architecture. In: Proceedings of 2010 ACM Symposium on Applied Computing, pp. 380–385 (2010)

    Google Scholar 

  13. Marinescu, D.C., Bai, X., Bölöni, L., Siegel, H.J., Daley, R.E., Wang, I.-J.: A macroeconomic model for resource allocation in large-scale distributed systems. J. Parallel Distrib. Comput. 68, 182–199 (2008)

    Article  MATH  Google Scholar 

  14. Marinescu, D.C., Siegel, H.J., Morrison, J.P.: Options and commodity markets for computing resources. In: Buyya, R., Bubendorf, K. (eds.) Market Oriented Grid and Utility Computing, pp. 89–120. Wiley, New York (2009). ISBN: 9780470287682

    Chapter  Google Scholar 

  15. Marinescu, D.C., Yu, C., Marinescu, G.M.: Scale-free, self-organizing very large sensor networks. J. Parallel Distrib. Comput. (JPDC) 50(5), 612–622 (2010)

    Article  Google Scholar 

  16. Marinescu, D.C.: Cloud Computing; Theory and Practice. Morgan Kaufmann, Boston (2013)

    Google Scholar 

  17. Marinescu, D.C.: High probability trajectories in the phase space and system complexity. Complex Syst. 22(3), 233–246 (2013)

    MathSciNet  Google Scholar 

  18. Marinescu, D.C., Paya, A., Morrison, J.P., Healy, P.: An auction-driven, self-organizing cloud delivery model, December 2013. http://arxiv.org/pdf/1312.2998v1.pdf

  19. Marinescu, D.C., Paya, A., Morrison, J.P., Healy, P.: Distributed hierarchical control versus an economic model for cloud resource management (2015). http://arxiv.org/pdf/1503.01061.pdf

  20. Marinescu, D.C., Paya, A., Morrison, J.P.: Coalition formation and combinatorial auctions; applications to self-organization and self-management in utility computing (2015). http://arXiv.org/pdf/1406.7487.pdf

  21. Mayer, M.W.: Architecting principles for system of systems. Syst. Eng. 1(4), 267–274 (1998)

    Article  Google Scholar 

  22. Minsky, M.: Computation: Finite and Infinite Machines. Prentice Hall, New York (1967)

    MATH  Google Scholar 

  23. Paton, N., de Arago, M.A.T., Lee, K., Fernandes, A.A.A., Sakellariou, R.: Optimizing utility in cloud computing through autonomic workload execution. Bull. Techn. Committee Data Eng. 32(1), 51–58 (2009)

    Google Scholar 

  24. Paya, A., Marinescu, D.C.: Energy-aware load balancing and application scaling for the cloud ecosystem. IEEE Trans. Cloud Comput. (2015). doi:10.1109/TCC.2015.2396059

  25. Snyder, B.: Server virtualization has stalled, despite the hype. http://www.infoworld.com/print/146901. Accessed 6 December 2013

  26. Sommerville, I., Cliff, D., Calinescu, R., Keen, J., Kelly, T., Kwiatowska, M., McDermid, J.: Large-scale IT complex systems. Commun. ACM 55(7), 71–77 (2012)

    Article  Google Scholar 

  27. Turing, A.M.: The chemical basis of morphogenesis. Philos. Trans. R. Soc. Lond. Ser. B 237, 37–72 (1952)

    Article  Google Scholar 

  28. Van, H.N., Tran, F.D., Menaud, J.M.: Autonomic virtual resource management for service hosting platforms. In: Software Engineering Challenges of Cloud Computing, ICSE Workshop at CLOUD 2009, pp. 1–8 (2009)

    Google Scholar 

  29. von Neumann, J.: Probabilistic logic and synthesis of reliable organisms from unreliable components. In: Shannon, C.E., McCarthy, J. (eds.) Automata Studies. Princeton University Press, Princeton (1956)

    Google Scholar 

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Authors and Affiliations

  1. Computer Science Division, Department of Electrical Engineering and Computer Science, University of Central Florida, Orlando, FL, USA

    Dan C. Marinescu & Ashkan Paya

  2. University College Cork, Cork, Ireland

    John P. Morrison

Authors
  1. Dan C. Marinescu
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  2. John P. Morrison
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  3. Ashkan Paya
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Corresponding author

Correspondence to Ashkan Paya .

Editor information

Editors and Affiliations

  1. University Politehnica of Bucharest, Bucharest, Romania

    Florin Pop

  2. Université Pierre et Marie CURIE, Paris, France

    Maria Potop-Butucaru

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Marinescu, D.C., Morrison, J.P., Paya, A. (2015). Is Cloud Self-organization Feasible?. In: Pop, F., Potop-Butucaru, M. (eds) Adaptive Resource Management and Scheduling for Cloud Computing. ARMS-CC 2015. Lecture Notes in Computer Science(), vol 9438. Springer, Cham. https://doi.org/10.1007/978-3-319-28448-4_9

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  • DOI: https://doi.org/10.1007/978-3-319-28448-4_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28447-7

  • Online ISBN: 978-3-319-28448-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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