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Quantitative Organic Computing

  • Christian Müller-Schloer
  • Sven Tomforde
Chapter
Part of the Autonomic Systems book series (ASYS)

Abstract

A practical implementation of OC concepts such as self-adaptivity and self-organisation into technical systems demands the augmentation of classical systems with special architectural components which make the self-optimisation possible. These architectures will be discussed in detail in Chap.  5 But before, we have to understand the basic underlying concepts. Some of those such as ‘emergence’ have been discussed for decades in a non-technical context. While this is certainly helpful, it is not sufficient for our purpose. In order to utilise and master self-organisation and adaptivity, we have to understand these notions quantitatively. Therefore, in this chapter we will try to define key terms precisely, wherever possible quantitatively, i.e. by formulas resulting in concrete numerical values which are then accessible to an automated analysis. Based on this analysis, the control mechanisms can make their decisions and steer the system under observation and control towards an optimised performance.

References

  1. [Bat68]
    K.E. Batcher, Sorting networks and their applications, in Proceedings of the April 30–May 2, 1968, Spring Joint Computer Conference (ACM, New York, 1968), pp. 307–314Google Scholar
  2. [Bra99]
    M.E. Bratman, Intention, Plans, and Practical Reason (Cambridge University Press, Cambridge, 1999)Google Scholar
  3. [Cal+00]
    D.S. Callaway, M.E. Newman, S.H. Strogatz, D.J. Watts, Network robustness and fragility: percolation on random graphs. Phys. Rev. Lett. 85(25), 5468 (2000)Google Scholar
  4. [Car89]
    N. Cartwright, Nature’s Capacities and Their Measurement (Clarendon, Oxford, 1989)Google Scholar
  5. [Cho+08]
    S. Choi, R. Buyya, H. Kim, E. Byun, J. Gil, A taxonomy of desktop grids and its mapping to state of the art systems, in Grid Computing and Distributed Systems Laboratory, The University of Melbourne, Tech. Rep. (2008)Google Scholar
  6. [CS15]
    L. Cherkasova K. Samuel, 2015 IEEE International Conference on Autonomic Computing, Grenoble, France (IEEE Computer Society, 2015). ISBN: 978-1-4673-6971-8. http://dblp.uni-trier.de/rec/bib/conf/icac/2015 Google Scholar
  7. [DH05]
    T. De Wolf, T. Holvoet, Emergence versus self-organisation: different concepts but promising when combined, in Engineering Self-organising Systems: Methodologies and Applications, ed. by S.A. Brueckner, G. Di Marzo Serugendo, A. Karageorgos, R. Nagpal (Springer, Berlin/Heidelberg, 2005), pp. 1–15. ISBN:978-3-540-31901-6Google Scholar
  8. [Ebe+15]
    B. Eberhardinger, G. Anders, H. Seebach, F. Siefert, W. Reif, A research overview and evaluation of performance metrics for self-organization algorithms, in 2015 IEEE International Conference on Self-adaptive and Self-organizing Systems Workshops (SASOW) (IEEE, Piscataway, NJ, 2015), pp. 122–127Google Scholar
  9. [Fis+10]
    D. Fisch, M. Janicke, B. Sick, C. Muller-Schloer, Quantitative emergence–a refined approach based on divergence measures, in 2010 Fourth IEEE International Conference on Self-adaptive and Self-organizing Systems (IEEE, Piscataway, NJ, 2010), pp. 94–103CrossRefGoogle Scholar
  10. [Fro04]
    J. Fromm, The Emergence of Complexity (Kassel University Press, Kassel, 2004). ISBN:3-89958-069-9, http://opac.inria.fr/record=b1118146 Google Scholar
  11. [GH03]
    C. Gershenson, F. Heylighen, When can we call a system self-organizing?, in Proceedings Advances in Artificial Life: 7th European Conference, ECAL 2003, Dortmund, Germany, 14–17 September 2003, ed. by W. Banzhaf, J. Ziegler, T. Christaller, P. Dittrich, J.T. Kim (Springer, Berlin/Heidelberg, 2003), pp. 606–614. ISBN: 978-3-540-39432-7CrossRefGoogle Scholar
  12. [HM09]
    R. Holzer, H. de Meer, Quantitative modeling of self-organizing properties, in International Workshop on Self-organizing Systems (Springer, New York, 2009), pp. 149–161Google Scholar
  13. [HM11]
    R. Holzer, H. de Meer, Methods for approximations of quantitative measures in self-organizing systems, in International Workshop on Self-organizing Systems (Springer, New York, 2011), pp. 1–15Google Scholar
  14. [HSS15]
    E. Hart, G. Sullivan, J.-P. Steghöfer, 2015 IEEE 9th International Conference on Self-Adaptive and Self-Organizing Systems, Cambridge, MA, USA (IEEE Computer Society, 2015). ISBN: 978-1-4673-7535-1Google Scholar
  15. [Jal94]
    P. Jalote, Fault Tolerance in Distributed Systems (Prentice-Hall, Upper Saddle River, NJ, 1994)Google Scholar
  16. [Kal16]
    I. Kaliszewski, Multiple Criteria Decision Making by Multiobjective Optimization: A Toolbox (Springer, New York, 2016)CrossRefzbMATHGoogle Scholar
  17. [Kan+16]
    J. Kantert, F. Reinhard, G.V. Zengen, S. Tomforde, S. Weber, L. Wolf, C. Mueller-Schloer, Combining trust and ETX to provide robust wireless sensor networks, in Workshop Proceedings of the 29th International Conference on Architecture of Computing Systems, ed. by A.L. Varbanescu, chap. 16 (VDE Verlag GmbH, Berlin/Offenbach, 2016), pp. 1–7. ISBN: 978-3-8007-4157-1Google Scholar
  18. [Kee95]
    L. Keeling, Feather pecking and cannibalism in layers. Poult. Int. 34(6), 46–49 (1995)Google Scholar
  19. [Knu98]
    D.E. Knuth, The Art of Computer Programming Volume 3: Sorting and Searching, 2nd ed. (Addison Wesley Longman, Redwood City, CA, 1998). ISBN: 0-201-89685-0Google Scholar
  20. [KTM15]
    J. Kantert, S. Tomforde, C. Mueller-Schloer, Measuring self-organisation in distributed systems by external observation, in Proceedings, ARCS 2015 – 28th International Conference Architecture of Computing Systems, VDE, pp. 1–8 (2015)Google Scholar
  21. [Küp90]
    B. Küppers, Der Ursprung biologischer Information: Zur Naturphilosophie der Lebensentstehung. Serie Piper (Piper, Munich, 1990). ISBN: 9783492113137Google Scholar
  22. [MBR05]
    D.A. Menascé, M.N. Bennani, H. Ruan, On the use of online analytic performance models, in self-managing and self-organizing computer systems, in Self-star Properties in Complex Information Systems (Springer, New York, 2005), pp. 128–142CrossRefGoogle Scholar
  23. [MM11a]
    M. Mnif, C. Müller-Schloer, Quantitative emergence, in Organic Computing – A Paradigm Shift for Complex Systems, ed. by C. Müller-Schloer, H. Schmeck, T. Ungerer (Springer, Basel, 2011), pp. 39–52. ISBN: 978-3-0348-0130-0CrossRefGoogle Scholar
  24. [MM11b]
    M. Mnif, C. Müller-Schloer, Quantitative emergence, in Organic Computing – A Paradigm Shift for Complex Systems (Springer, Basel, 2011), pp. 39–52CrossRefGoogle Scholar
  25. [MSU11]
    C. Müller-Schloer, H. Schmeck, T. Ungerer, Organic Computing – A Paradigm Shift for Complex Systems, 1st edn. (Springer, Berlin/Heidelberg, 2011)CrossRefzbMATHGoogle Scholar
  26. [Mue+07]
    G. Muehl, M. Werner, M.A. Jaeger, K. Herrmann, H. Parzyjegla, On the definitions of self-managing and self-organizing systems, in 2007 ITG-GI Conference Communication in Distributed Systems (KiVS), pp. 1–11 (2007)Google Scholar
  27. [MV97]
    L. Ming, P. Vitányi, An Introduction to Kolmogorov Complexity and Its Applications (Springer, Heidelberg, 1997)zbMATHGoogle Scholar
  28. [Naf+11a]
    F. Nafz, H. Seebach, J.-P. Steghöfer, G. Anders, W. Reif, Constraining self-organisation through corridors of correct behaviour: the restore invariant approach, in Organic Computing – A Paradigm Shift for Complex Systems (Springer, Berlin, 2011), pp. 79–93Google Scholar
  29. [Naf+11b]
    F. Nafz, H. Seebach, J.-P. Steghöfer, G. Anders, W. Reif, Constraining self-organisation through corridors of correct behaviour: the restore invariant approach, in Organic Computing – A Paradigm Shift for Complex Systems (Springer, Berlin, 2011), pp. 79–93Google Scholar
  30. [NL04]
    J. Nimis, P.C. Lockemann, Robust multi-agent systems the transactional conversation approach, in First International Workshop on Safety and Security in Multiagent Systems, pp. 73–84 (2004)Google Scholar
  31. [Pro+08]
    H. Prothmann, F. Rochner, S. Tomforde, J. Branke, C. Müller-Schloer, H. Schmeck, Organic control of traffic lights, in Proceedings of the 5th International Conference on Autonomic and Trusted Computing (ATC-08), Held in Oslo, Norway 23–25 June 2008, ed. by C. Rong et al. LNCS, vol. 5060 (Springer, New York, 2008), pp. 219–233Google Scholar
  32. [Rey87]
    C.W. Reynolds, Flocks, herds and schools: a distributed behavioral model. ACM SIGGRAPH Comput. Graph. 21(4), 25–34 (1987)CrossRefGoogle Scholar
  33. [Rud+16]
    S. Rudolph, J. Kantert, U. Jaenen, S. Tomforde, J. Haehner, C. Mueller-Schloer, Measuring self-organisation processes in smart camera networks, in ARCS 2016 (2016)Google Scholar
  34. [S+91]
    J.-J.E. Slotine, W. Li et al., Applied Nonlinear Control, vol. 1991 (Prentice-Hall, Englewood Cliffs, NJ, 1991)zbMATHGoogle Scholar
  35. [Sch+00]
    A. Scholl et al., Robuste Planung und Optimierung: Grundlagen, Konzepte und Methoden; Experimentelle Untersuchungen (2000)Google Scholar
  36. [Sch+10]
    H. Schmeck, C. Müller-Schloer, E. Çakar, M. Mnif, U. Richter, Adaptivity and self-organization in organic computing systems. ACM Trans. Auton. Adapt. Syst. 5(3), 10:1–10:32 (2010). ISSN: 1556–4665Google Scholar
  37. [Sch+11]
    H. Schmeck, C. Müller-Schloer, E. Çakar, M. Mnif, U. Richter, Adaptivity and self-organisation in organic computing systems, in Organic Computing – A Paradigm Shift for Complex Systems, ed. by C. Müller-Schloer, H. Schmeck, T. Ungerer (Springer, Basel, 2011), pp. 5–37. ISBN: 978-3-0348-0130-0CrossRefGoogle Scholar
  38. [Ser09]
    G.D.M. Serugendo, Robustness and dependability of self-organizing systems-a safety engineering perspective, in Symposium on Self-Stabilizing Systems (Springer, New York, 2009), pp. 254–268Google Scholar
  39. [Ste98]
    A. Stephan, Varieties of emergence in artificial and natural systems. Z. Naturforsch. C 53(7–8), 639–656 (1998)Google Scholar
  40. [Tag93]
    G. Taguchi, Robust technology development. In Journal of Mechanical Engineering-CIME, publisher American Society of Mechanical Engineers 115(3), 60–63 (1993)Google Scholar
  41. [TKS17]
    S. Tomforde, J. Kantert, B. Sick, Measuring self-organisation at runtime: a quantification method based on divergence measures, in Proceedings of the 9th International Conference in Agents and Artificial Intelligence (ICAART’17), Held in Porto, Portugal, 24–26 February 2017 (SciTePress, Portugal, 2017), pp. 96–106Google Scholar
  42. [TM71]
    M. Tribus, E.C. McIrvine, Energy and information. Sci. Am. 225(3), 179–188 (1971)CrossRefGoogle Scholar
  43. [Tom+10]
    S. Tomforde, H. Prothmann, J. Branke, J. Hähner, C. Müller-Schloer, H. Schmeck, Possibilities and limitations of decentralised traffic control systems, The 2010 International Joint Conference on Neural Networks (IJCNN) (IEEE, Piscataway, NJ, 2010), pp. 1–9CrossRefGoogle Scholar
  44. [Tom+11]
    S. Tomforde, H. Prothmann, J. Branke, J. Hähner, M. Mnif, C. Müller-Schloer, U. Richter, H. Schmeck, Observation and control of organic systems, Organic Computing – A Paradigm Shift for Complex Systems (Springer, Basel, 2011), pp. 325–338CrossRefGoogle Scholar
  45. [Win12]
    T. Winter, RPL: IPv6 routing protocol for low-power and lossy networks, Internet Engineering Task Force, Mar. 2012. (online) www.ietf.org (2012)
  46. [Wri+00]
    W. Wright, R.E. Smith, M. Danek, P. Greenway, S. Centre, A measure of emergence in an adapting, multi-agent context, in Proceedings of the 6th International Conference on the Simulation of Adaptive Behaviour (SAB’00) (Springer, New York, 2000)Google Scholar
  47. [Wri+01]
    W.A. Wright, R.E. Smith, M. Danek, P. Greenway, A generalisable measure of self-organisation and emergence, in Proceedings Artificial Neural Networks – ICANN 2001: International Conference Vienna, Austria, 21–25 Aug 2001, ed. by G. Dorffner, H. Bischof, K. Hornik (Springer, Berlin/Heidelberg, 2001), pp. 857–864. ISBN: 978-3-540-44668-2CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Christian Müller-Schloer
    • 1
  • Sven Tomforde
    • 2
  1. 1.Institute of Systems EngineeringLeibniz Universität HannoverHannoverGermany
  2. 2.Intelligent Embedded Systems GroupUniversität KasselKasselGermany

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