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Autonomic Systems

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Autonomic Computing

Part of the book series: Undergraduate Topics in Computer Science ((UTICS))

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Abstract

The purpose of this chapter is to define the notion of autonomic systems and to introduce related terminology. It discusses the main ideas that are essential to any autonomic computing system, including the concepts of ‘goal’, ‘context’ and ‘self-*’ capabilities.

The chapter also presents the initial motivations behind the autonomic computing initiative. It subsequently discusses the relevance of these motivations in light of both research and real-world implementations since this initiative was launched in the early days of the millennium.

We highlight the most important benefits that autonomic computing promises to bring to the IT domain as well as the equally important challenges that must be surpassed before computer systems can be endowed with autonomic management capabilities. An incremental approach to autonomic computing is presented in this context, proposing a five-step roadmap for progressively transforming current IT systems from their current (non-autonomic) status to full autonomic management support.

Finally, the chapter aims to position the relatively new autonomic computing initiative with respect to similar technological fields, supported by industry, governments or academia, as well as with respect to existing computing domains. Further relevant fields are discussed in the following chapter highlighting the inspiration that autonomic computing has and can draw from existing domains.

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Notes

  1. 1.

    Definitions based on combined, adapted input from Merriam-Webster’s online dictionary, 11th edition—http://www.merriam-webster.com, American Heritage Dictionary of the English Language and Oxford Dictionaries—http://oxforddictionaries.com

  2. 2.

    While a discussion on such matters would be well outside the scope of the current publication, it could raise useful considerations regarding the purposes and limitations of the autonomous systems we are going to build.

  3. 3.

    The i* agent- and goal-oriented modelling framework: http://www.cs.toronto.edu/km/istar

  4. 4.

    The Tropos project: http://www.troposproject.org

  5. 5.

    One could say that autonomic computing is a marriage of many subjects; therefore, it is no surprise that many of the early proponents of the field from IBM originated in physics (e.g. Paul Horn), computer systems (David Chess) and agent-based computing (Jeffrey Kephart).

  6. 6.

    While decreasing numbers of farmers could also be caused by factors other than technology, such as massive food imports, the US Department of Agriculture (USDA) provides data indicating clear increases in farming productivity throughout the US history. For example, data available in this USDA article—National Institute of Food and Agriculture: http://www.csrees.usda.gov/qlinks/extension.html—points out that producing 100 bushels of corn necessitated around 14 labour hours and 2 acres of land in 1945, under 3 labour hours and little over 1 acre in 1987 and less than 1 acre of land in 2002. This and a discussion on bioengineered food are well beyond the scope of this book.

  7. 7.

    Alfred North Whitehead (1861–1947)—English mathematician and philosopher.

  8. 8.

    Bureau of Labour Statistics—United States Department of Labour: http://www.bls.gov

  9. 9.

    The fastest-growing groups being may be not surprisingly related to healthcare.

  10. 10.

    TCG—the Trusted Computing GroupTM (http://www.trustedcomputing.org)—a non-profit organisation formed to develop and promote open, vendor-neutral standards and frameworks for supporting trusted computing technology. The goal of trusted computing technology is to render computer systems safer and less prone to viruses, malware and unauthorised access.

  11. 11.

    ‘Compaq Redefines IT Landscape with Adaptive Infrastructure’, December 2001—http://www.hp.com/hpinfo/newsroom/press/2001pmc/pr2001120501.html

  12. 12.

    HP Converged Infrastructure : http://www.hp.com/go/ci

  13. 13.

    ‘Sun Introduces N1 Architecture, Looks To Manage Networks As A Single System’, by Joseph F. Kovar, CRN, September 2002 – http://www.crn.com/news/channel-programs/18821653/sun-­introduces-n1-architecture-looks-to-manage-networks-as-a-single-system.htm

  14. 14.

    ‘Microsoft Announces Dynamic Systems Initiative’, March 2003—http://www.microsoft.com/en-us/news/press/2003/mar03/03-18dynamicsystemspr.aspx

  15. 15.

    http://www.networkworld.com/news/2007/072407-cisco-new-data-center.html

  16. 16.

    Introduction to Ambient Intelligence from ERCIM News 2001: http://www.ercim.eu/publication/Ercim_News/enw47/intro.html

  17. 17.

    DARPA: Defence Advanced Research Projects Agency—http://www.darpa.mil

  18. 18.

    Henry S. Kenyon, ‘Networks: Adapting to Uncertainty’, DARPA, http://www.darpa.mil/WorkArea/DownloadAsset.aspx?id=2570

  19. 19.

    DARPA ‘s Assured Arctic Awareness (AAA) program: ­http://www.darpa.mil/NewsEvents/Releases/2012/03/16a.aspx

  20. 20.

    DARPA’s announcement (April 2012) of future Robotic Challenge program (to be launched in October 2012): http://www.darpa.mil/NewsEvents/Releases/2012/04/10.aspx

  21. 21.

    DARPA’s Urban Challenge, held in November 2007, at the former George Air Force Base in Victorville, California, USA—http://archive.darpa.mil/grandchallenge

  22. 22.

    NASA: National Aeronautics and Space Administration—http://www.nasa.gov

  23. 23.

    AutoNav: NASA’s autonomous navigation system uses the relative positions of well-known bright asteroids with respect to a background star for positioning a spacecraft and guiding it to its destination. AutoNav has been successfully tested for guiding the Deep Space 1 spacecraft (1999) and Deep Impact (2005).

  24. 24.

    NASA’s Mars Science Laboratory (MSL) mission: http://marsprogram.jpl.nasa.gov/msl

  25. 25.

    ESA: European Space Agency—http://www.esa.int

  26. 26.

    Mars Sample Return mission: http://www.esa.int/esaMI/Aurora/SEM1PM808BE_0.html

  27. 27.

    NASA’s Autonomous Nano Technology Swarm (ANTS) program—http://ants.gsfc.nasa.gov

  28. 28.

    NASA’s ANTS Prospecting Asteroid Mission (PAM), expected timeframe: 2020–2025, http://ants.gsfc.nasa.gov/pam.html

  29. 29.

    Organic computing (OC) initiative: http://www.organic-computing.de. The OC initiative has been launched by a group of researchers from three German universities (Universität Hannover, Universität Karlsruhe and Universität Augsburg). In 2012 the initiative comprised more than 70 researchers from many institutions across Germany and other European and non-European countries. OC has been initially funded by the German Research Foundation (DFG) as part of the priority programme 1183 organic computing (2004–2011).

  30. 30.

    Amorphous computing project, defined at MIT: http://groups.csail.mit.edu/mac/projects/amorphous

  31. 31.

    Multicellular Computing Website: http://www.evolutionofcomputing.org

  32. 32.

    ‘Morphogenetic engineering weds bio self-organisation to human-designed systems’, R. Doursat, PerAda Magazine: Towards Pervasive Adaptation, 18 May 2011—http://www.perada-magazine.eu/view.php?article=003722-2011-05-18&category=Agents

  33. 33.

    The chemical metaphor as programming paradigm for organic computing: ­http://users.minet.uni-jena.de/csb/prj/organic

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Author information

Authors and Affiliations

  1. Laboratoire Informatique de Grenoble, Université Joseph Fourier, Grenoble, France

    Philippe Lalanda

  2. Department of Computing, Imperial College London, London, UK

    Julie A. McCann

  3. Department of Computing and Networking, Télécom ParisTech, Paris, France

    Ada Diaconescu

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  1. Philippe Lalanda
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  2. Julie A. McCann
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  3. Ada Diaconescu
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Lalanda, P., McCann, J.A., Diaconescu, A. (2013). Autonomic Systems. In: Autonomic Computing. Undergraduate Topics in Computer Science. Springer, London. https://doi.org/10.1007/978-1-4471-5007-7_2

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  • DOI: https://doi.org/10.1007/978-1-4471-5007-7_2

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