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Architectures & Infrastructure

  • Françoise André
  • Ivona Brandic
  • Erwan Daubert
  • Guillaume Gauvrit
  • Maurizio Giordano
  • Gabor Kecskemeti
  • Attila Kertész
  • Claudia Di Napoli
  • Zsolt Nemeth
  • Jean-Louis Pazat
  • Harald Psaier
  • Wolfgang Renz
  • Jan Sudeikat
Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6500)

Abstract

The third of the S-Cube technology layers provides infrastructure capabilities for defining basic communication patterns and interactions involving as well as providing facilities for providing, for example, contextual and qualitative information about a service’s and their client’s environment and performance. Providing these capabilities to other layers allows service developers to use contextual information when building service based systems and provide cross layer and pro-active monitoring and adaptation of services (see research challenges). This chapter provides an overview of service infrastructures for the adaptation, monitoring and management of services which will provide these functions and concludes with a discussion of more detailed research challenges in the context of service infrastructures and their management.

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References

  1. 1.
    Abraham, A., Liu, H., Grosan, C., Xhafa, F.: Nature inspired meta-heuristics for grid scheduling: Single and multi-objective optimization approaches. In: Xhafa, F., Abraham, A. (eds.) Metaheuristics for Scheduling in Distributed Computing Environments, pp. 247–272. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  2. 2.
    Abraham, A., Liu, H., Zhao, M.: Particle swarm scheduling for work-flow applications in distributed computing environments. In: Metaheuristics for Scheduling in Industrial and Manufacturing Applications, pp. 327–342 (2008)CrossRefGoogle Scholar
  3. 3.
    Artsy, Y., Finkel, R.: Designing a process migration facility: the charlotte experience. Computer 22(9), 47–56 (1989)CrossRefGoogle Scholar
  4. 4.
    Babaoglu, O., Canright, G., Deutsch, A., Di Caro, G.A., Ducatelle, F., Gambardella, L.M., Ganguly, N., Jelasity, M., Montemanni, R., Montresor, A., Urnes, T.: Design patterns from biology for distributed computing. ACM Transactions on Autonomous and Adaptive Systems 1(1), 26–66 (2006)CrossRefGoogle Scholar
  5. 5.
    Banâtre, J.-P., Priol, T.: Chemical programming of future service-oriented architectures. JSW 4(7), 738–746 (2009)CrossRefGoogle Scholar
  6. 6.
    Banâtre, J.-P., Priol, T., Radenac, Y.: Service orchestration using the chemical metaphor. In: Brinkschulte, U., Givargis, T., Russo, S. (eds.) SEUS 2008. LNCS, vol. 5287, pp. 79–89. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  7. 7.
    Baresi, L., Guinea, S., Pasquale, L.: Self-healing bpel processes with dynamo and the jboss rule engine. In: ESSPE, pp. 11–20 (2007)Google Scholar
  8. 8.
    Bigus, J.P., Schlosnagle, D.A., Pilgrim III., J.R., Mills, W.N., Diao, Y.: Able: A toolkit for building multiagent autonomic systems. IBM Systems Journal 41(3), 350–371 (2002)CrossRefGoogle Scholar
  9. 9.
    Blair, G.S., Coulson, G., Blair, L., Duran-Limon, H., Grace, P., Moreira, R., Parlavantzas, N.: Reflection, self-awareness and self-healing in openorb. In: WOSS, pp. 9–14 (2002)Google Scholar
  10. 10.
    Bonabeau, E., Dorigo, M., Theraulaz, G.: Inspiration for optimization from social insect behaviour. Nature 406, 39–42 (2000)CrossRefGoogle Scholar
  11. 11.
    Brits, R., Engelbrecht, A.P., van den Bergh, F.: Locating multiple optima using particle swarm optimization. Applied Mathematics and Computation 189(2), 1859–1883 (2007)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Brueckner, S., Czap, H.: Organization, self-organization, autonomy and emergence: Status and challenges. International Transactions on Systems Science and Applications 2(1), 1–9 (2006)Google Scholar
  13. 13.
    Canfora, G., Di Penta, M., Esposito, R., Villani, M.L.: An approach for qos-aware service composition based on genetic algorithms. In: GECCO ’05: Proceedings of the 2005 conference on Genetic and evolutionary computation, New York, NY, USA, pp. 1069–1075. ACM Press (2005)Google Scholar
  14. 14.
    Champrasert, P., Lee, C., Suzuki, J.: Symbioticsphere: Towards an autonomic grid network system. In: CLUSTER, pp. 1–2 (2005)Google Scholar
  15. 15.
    Champrasert, P., Suzuki, J.: A biologically-inspired autonomic architecture for self-healing data centers. In: COMPSAC (1), pp. 103–112 (2006)Google Scholar
  16. 16.
    Champrasert, P., Suzuki, J.: Symbioticsphere: A biologically-inspired autonomic architecture for self-managing network systems. In: COMPSAC (2), pp. 350–352 (2006)Google Scholar
  17. 17.
    Cheng, S.-W., Garlan, D., Schmerl, B.R., Sousa, J.P., Spitnagel, B., Steenkiste, P.: Using architectural style as a basis for system self-repair. In: WICSA, pp. 45–59 (2002)Google Scholar
  18. 18.
    Corsava, S., Getov, V.: Intelligent architecture for automatic resource allocation in computer clusters. In: IPDPS, p. 201.1 (2003)Google Scholar
  19. 19.
    Csorba, M.J., Heegaard, P.E.: Swarm intelligence heuristics for component deployment. In: EUNICE. LNCS, vol. 6164, pp. 51–64. Springer, Heidelberg (2010)Google Scholar
  20. 20.
    Csorba, M.J., Meling, H., Heegaard, P.E.: Ant system for service deployment in private and public clouds. In: BADS ’10: Proceeding of the 2nd workshop on Bio-inspired algorithms for distributed systems, New York, NY, USA, pp. 19–28. ACM (2010)Google Scholar
  21. 21.
    Csorba, M.J., Meling, H., Heegaard, P.E., Herrmann, P.: Foraging for better deployment of replicated service components. In: DAIS ’09: Proceedings of the 9th IFIP WG 6.1 International Conference on Distributed Applications and Interoperable Systems. LNCS, vol. 5523, pp. 87–101. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  22. 22.
    Dasgupta, D.: Advances in artificial immune systems. IEEE Computational Intelligence Magazine, 40–49 (Nov. 2006)CrossRefGoogle Scholar
  23. 23.
    Dasgupta, D., González, F.A.: An immunity-based technique to characterize intrusions in computer networks. IEEE Trans. Evolutionary Computation 6(3), 281–291 (2002)CrossRefGoogle Scholar
  24. 24.
    Dashofy, E.M., van der Hoek, A., Taylor, R.N.: Towards architecture-based self-healing systems. In: WOSS, pp. 21–26 (2002)Google Scholar
  25. 25.
    Devescovi, D., Di Nitto, E., Dubois, D., Mirandola, R.: Self-organization algorithms for autonomic systems in the selflet approach. In: Autonomics ’07: Proceedings of the 1st international conference on Autonomic computing and communication systems, pp. 1–10, ICST, Brussels, Belgium, Belgium, 2007. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering) (2007)Google Scholar
  26. 26.
    Dijkstra, E.W.: Self-stabilizing systems in spite of distributed control. Commun. ACM 17(11), 643–644 (1974)CrossRefGoogle Scholar
  27. 27.
    Ding, Y., Sun, H., Hao, K.: A bio-inspired emergent system for intelligent web service composition and management. Knowledge-Based Systems 20, 457–465 (2007)CrossRefGoogle Scholar
  28. 28.
    Dorigo, M.: Ant algorithms solve difficult optimization problems. In: Kelemen, J., Sosík, P. (eds.) ECAL 2001. LNCS (LNAI), vol. 2159, pp. 11–22. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  29. 29.
    Dorigo, M., Bonabeau, E., Theraulaz, G.: Ant algorithms and stigmergy. Future Gener. Comput. Syst. 16(9), 851–871 (2000)CrossRefGoogle Scholar
  30. 30.
    Dorigo, M., Di Caro, G., Gambardella, L.M.: Ant algorithms for discrete optimization. Artificial Life 5(2), 137–172 (1999)CrossRefGoogle Scholar
  31. 31.
    Douglis, F., Ousterhout, J.: Transparent process migration: Design alternatives and the sprite implementation. Software - Practice and Experience 21, 757–785 (1991)CrossRefGoogle Scholar
  32. 32.
    Floreano, D., Mattiussi, C.: Bio-Inspired Artificial Intelligence Theories, Methods, and Technologies. MIT Press, Cambridge (Sept. 2008)Google Scholar
  33. 33.
    Forrest, S.: Genetic algorithms. ACM Comput. Surv. 28(1), 77–80 (1996)CrossRefGoogle Scholar
  34. 34.
    Freitas, A.A., Timmis, J.: Revisiting the foundations of artificial immune systems for data mining. IEEE Trans. Evolutionary Computation 11(4), 521–540 (2007)CrossRefGoogle Scholar
  35. 35.
    Ganek, A.G., Corbi, T.A.: The dawning of the autonomic computing era. IBM Syst. J. 42(1), 5–18 (2003)CrossRefGoogle Scholar
  36. 36.
    Ghallab, M., Ecole Nationale, Constructions Aeronautiques, Isi, C.K., Penberthy, S., Smith, D.E., Sun, Y., Weld, D.: Pddl - the planning domain definition language. Technical report (1998)Google Scholar
  37. 37.
    Ghosh, D., Sharman, R., Rao, H.R., Upadhyaya, S.: Self-healing systems - survey and synthesis. Decission Support Systems 42(4), 2164–2185 (2007)CrossRefGoogle Scholar
  38. 38.
    Glass, M., Lukasiewycz, M., Reimann, F., Haubelt, C.D., Teich, J.: Symbolic reliability analysis of self-healing networked embedded systems. In: Harrison, M.D., Sujan, M.-A. (eds.) SAFECOMP 2008. LNCS, vol. 5219, pp. 139–152. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  39. 39.
    Halima, R.B., Drira, K., Jmaiel, M.: A QoS-Oriented Reconfigurable Middleware for Self-Healing Web Services. In: ICWS, pp. 104–111 (2008)Google Scholar
  40. 40.
    Hinchey, M.G., Sterritt, R., Rouff, C.A.: Swarms and swarm intelligence. IEEE Computer 40(4), 111–113 (2007)CrossRefGoogle Scholar
  41. 41.
    Hossain, M.S., Alamri, A., El-Saddik, A.: A biologically inspired framework for multimedia service management in a ubiquitous environment. Concurrency and Computation: Practice and Experience 21(11), 1450–1466 (2009)CrossRefGoogle Scholar
  42. 42.
    Huebscher, M.C., McCann, J.A.: A survey of autonomic computing—degrees, models, and applications. ACM Comput. Surv. 40(3), 1–28 (2008)CrossRefGoogle Scholar
  43. 43.
    Jennings, N.R.: Building complex, distributed systems: the case for an agent-based approach. Comms. of the ACM 44(4), 35–41 (2001)CrossRefGoogle Scholar
  44. 44.
    Kephart, J.O.: Research challenges of autonomic computing. In: ICSE ’05: Proceedings of the 27th international conference on Software engineering, pp. 15–22. ACM Press, New York (2005)Google Scholar
  45. 45.
    Kephart, J.O., Chess, D.M.: The vision of autonomic computing. IEEE Computer 36(1), 41–50 (2003)CrossRefGoogle Scholar
  46. 46.
    Kirkpatrick, S., Gelatt Jr., D., Vecchi, M.P.: Optimization by simmulated annealing. Science 220(4598), 671–680 (1983)MathSciNetCrossRefGoogle Scholar
  47. 47.
    Lee, C., Suzuki, J.: An immunologically-inspired autonomic framework for self-organizing and evolvable network applications. TAAS 4(4) (2009)CrossRefGoogle Scholar
  48. 48.
    Mei, L., Chan, W.K., Tse, T.H.: An adaptive service selection approach to service composition. In: Proceedings of the IEEE International Conference on Web Services (ICWS 2008), IEEE Computer Society Press, Los Alamitos (2008)Google Scholar
  49. 49.
    Mogul, J.C.: Emergent (mis)behavior vs. complex software systems. Technical Report HPL-2006-2, HP Laboratories Palo Alto (2005)Google Scholar
  50. 50.
    Olariu, S., Zomaya, A.Y. (eds.): Handbook of Bioinspired Algorithms and Applications. CRC Press, Boca Raton (2005)zbMATHGoogle Scholar
  51. 51.
    Pandey, S., Wu, L., Guru, S.M., Buyya, R.: A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments. In: AINA, pp. 400–407 (2010)Google Scholar
  52. 52.
    Pierce, W.H.: Failure-tolerant Computer Design. Academic Press, London (1965)Google Scholar
  53. 53.
    Prokopenko, M.: Design vs. Self-organization. In: Prokopenko, M. (ed.) Advances in Applied Self-organizing Systems, pp. 3–17. Springer, London (2008)CrossRefGoogle Scholar
  54. 54.
    Psaier, H., Dustdar, S.: A survey on self-healing systems - approaches and systems. Computing 87(1) (2010)CrossRefGoogle Scholar
  55. 55.
    Saffre, F., Halloy, J., Shackleton, M., Deneubourg, J.L.: Self-organized service orchestration through collective differentiation. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 36(6), 1237–1246 (2006)CrossRefGoogle Scholar
  56. 56.
    Salehie, M., Tahvildari, L.: Self-adaptive software: Landscape and research challenges. ACM Trans. Auton. Adapt. Syst. 4(2), 1–42 (2009)CrossRefGoogle Scholar
  57. 57.
    Salleh, S., Sanugi, B., Jamaluddin, H., Olariu, S., Zomaya, A.Y.: Enhanced simulated annealing technique for the single-row routing problem. The Journal of Supercomputing 21(3), 285–302 (2002)CrossRefGoogle Scholar
  58. 58.
    Seiter, L.M., Palmer, D.W., Kirschenbaum, M.: An aspect-oriented approach for modeling self-organizing emergent structures. In: SELMAS ’06: Proceedings of the 2006 international workshop on Software engineering for large-scale multi-agent systems, pp. 59–66. ACM Press, New York (2006)CrossRefGoogle Scholar
  59. 59.
    Serugendo, G.D.M., Gleizes, M.P., Karageorgos, A.: Self-organisation and emergence in mas: An overview. Informatica 30, 45–54 (2006)zbMATHGoogle Scholar
  60. 60.
    Di Marzo Serugendo, G., Fitzgerald, J.: Designing and controlling trustworthy self-organising systems. Perada Magazine (2009)Google Scholar
  61. 61.
    Shapiro, M.W.: Self-healing in modern operating systems. ACM Queue 2(9), 66–75 (2005)CrossRefGoogle Scholar
  62. 62.
    Stellner, G.: Cocheck: checkpointing and process migration for mpi. In: The 10th International Parallel Processing Symposium, 1996, Proceedings of IPPS ’96, Apr. 1996, pp. 526–531 (1996)Google Scholar
  63. 63.
    Sterritt, R.: Autonomic computing. Innovations in Systems and Software Engineering 1(1), 79–88 (2005)CrossRefGoogle Scholar
  64. 64.
    Sudeikat, J., Braubach, L., Pokahr, A., Renz, W., Lamersdorf, W.: Systematically engineering self-organizing systems: The sodekovs approach. Electronic Communications of the EASST 17 (2009)Google Scholar
  65. 65.
    Sudeikat, J., Renz, W.: MASDynamics: Toward systemic modeling of decentralized agent coordination. In: David, K., Geihs, K. (eds.) Kommunikation in Verteilten Systemen. Informatik aktuell, pp. 79–90. Springer, Heidelberg (2009)Google Scholar
  66. 66.
    Sudeikat, J., Renz, W.: Programming adaptivity by complementing agent function with agent coordination: A systemic programming model and development methodology integration. Communications of SIWN 7, 91–102 (2009)Google Scholar
  67. 67.
    Sudeikat, J., Renz, W.: Shoaling glassfishes: Enabling decentralized web service management. In: 3rd International Conference in Sef-Adaptive and Self-Organizing Systems, pp. 291–292 (short paper). IEEE Computer Society Press, Los Alamitos (2009)Google Scholar
  68. 68.
    Sun, H., Ding, Y.: A scalable method of e-service workflow emergence based on the bio-network. In: Fourth International Conference on Natural Computation (2008)Google Scholar
  69. 69.
    Swiecicka, A., Seredynski, F., Zomaya, A.Y.: Multiprocessor scheduling and rescheduling with use of cellular automata and artificial immune system support. IEEE Trans. Parallel Distrib. Syst. 17(3), 253–262 (2006)CrossRefGoogle Scholar
  70. 70.
    Taheri, J., Zomaya, A.Y.: A simulated annealing approach for mobile location management. Computer Communications 30(4), 714–730 (2007)CrossRefGoogle Scholar
  71. 71.
    Tesauro, G., Chess, D.M., Walsh, W.E., Das, R., Segal, A., Whalley, I., Kephart, J.O., White, S.R.: A multi-agent systems approach to autonomic computing. In: AAMAS, pp. 464–471 (2004)Google Scholar
  72. 72.
    Vanrompay, Y., Rigole, P., Berbers, Y.: Genetic algorithm-based optimization of service composition and deployment. In: SIPE ’08: Proceedings of the 3rd international workshop on Services integration in pervasive environments, New York, NY, pp. 13–18. ACM (2008)Google Scholar
  73. 73.
    Viroli, M., Zambonelli, F.: A biochemical approach to adaptive service ecosystems. Inform. Sci. (2009)Google Scholar
  74. 74.
    Viroli, M., Holvoet, T., Ricci, A., Schelfthout, K., Zambonelli, F.: Infrastructures for the environment of multiagent systems. Autonomous Agents and Multi-Agent Systems 14(1), 49–60 (2007)CrossRefGoogle Scholar
  75. 75.
    Weyns, D., Holvoet, T.: An architectural strategy for self-adapting systems. In: SEAMS ’07: Proceedings of the 2007 International Workshop on Software Engineering for Adaptive and Self-Managing Systems, Washington, DC, USA, IEEE Computer Society (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Françoise André
    • 1
  • Ivona Brandic
    • 2
  • Erwan Daubert
    • 1
  • Guillaume Gauvrit
    • 1
  • Maurizio Giordano
    • 3
  • Gabor Kecskemeti
    • 4
  • Attila Kertész
    • 4
  • Claudia Di Napoli
    • 3
  • Zsolt Nemeth
    • 4
  • Jean-Louis Pazat
    • 1
  • Harald Psaier
    • 2
  • Wolfgang Renz
    • 5
  • Jan Sudeikat
    • 5
    • 6
  1. 1.Institut National de Recherche en Informatique et Automatique (INRIA)France
  2. 2.Technische Universität WienViennaAustria
  3. 3.Consiglio Nazionale delle Ricerche (CNR)NaplesItaly
  4. 4.MTA Computer & Automation Research Institute (MTA-SZTAKI)BudapestHungary
  5. 5.Multimedia Systems Lab. (MMLab)Hamburg University of Applied SciencesGermany
  6. 6.Department of InformaticsUniversity of HamburgGermany

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