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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
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)
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)
Artsy, Y., Finkel, R.: Designing a process migration facility: the charlotte experience. Computer 22(9), 47–56 (1989)
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)
Banâtre, J.-P., Priol, T.: Chemical programming of future service-oriented architectures. JSW 4(7), 738–746 (2009)
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)
Baresi, L., Guinea, S., Pasquale, L.: Self-healing bpel processes with dynamo and the jboss rule engine. In: ESSPE, pp. 11–20 (2007)
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)
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)
Bonabeau, E., Dorigo, M., Theraulaz, G.: Inspiration for optimization from social insect behaviour. Nature 406, 39–42 (2000)
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)
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)
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)
Champrasert, P., Lee, C., Suzuki, J.: Symbioticsphere: Towards an autonomic grid network system. In: CLUSTER, pp. 1–2 (2005)
Champrasert, P., Suzuki, J.: A biologically-inspired autonomic architecture for self-healing data centers. In: COMPSAC (1), pp. 103–112 (2006)
Champrasert, P., Suzuki, J.: Symbioticsphere: A biologically-inspired autonomic architecture for self-managing network systems. In: COMPSAC (2), pp. 350–352 (2006)
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)
Corsava, S., Getov, V.: Intelligent architecture for automatic resource allocation in computer clusters. In: IPDPS, p. 201.1 (2003)
Csorba, M.J., Heegaard, P.E.: Swarm intelligence heuristics for component deployment. In: EUNICE. LNCS, vol. 6164, pp. 51–64. Springer, Heidelberg (2010)
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)
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)
Dasgupta, D.: Advances in artificial immune systems. IEEE Computational Intelligence Magazine, 40–49 (Nov. 2006)
Dasgupta, D., González, F.A.: An immunity-based technique to characterize intrusions in computer networks. IEEE Trans. Evolutionary Computation 6(3), 281–291 (2002)
Dashofy, E.M., van der Hoek, A., Taylor, R.N.: Towards architecture-based self-healing systems. In: WOSS, pp. 21–26 (2002)
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)
Dijkstra, E.W.: Self-stabilizing systems in spite of distributed control. Commun. ACM 17(11), 643–644 (1974)
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)
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)
Dorigo, M., Bonabeau, E., Theraulaz, G.: Ant algorithms and stigmergy. Future Gener. Comput. Syst. 16(9), 851–871 (2000)
Dorigo, M., Di Caro, G., Gambardella, L.M.: Ant algorithms for discrete optimization. Artificial Life 5(2), 137–172 (1999)
Douglis, F., Ousterhout, J.: Transparent process migration: Design alternatives and the sprite implementation. Software - Practice and Experience 21, 757–785 (1991)
Floreano, D., Mattiussi, C.: Bio-Inspired Artificial Intelligence Theories, Methods, and Technologies. MIT Press, Cambridge (Sept. 2008)
Forrest, S.: Genetic algorithms. ACM Comput. Surv. 28(1), 77–80 (1996)
Freitas, A.A., Timmis, J.: Revisiting the foundations of artificial immune systems for data mining. IEEE Trans. Evolutionary Computation 11(4), 521–540 (2007)
Ganek, A.G., Corbi, T.A.: The dawning of the autonomic computing era. IBM Syst. J. 42(1), 5–18 (2003)
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)
Ghosh, D., Sharman, R., Rao, H.R., Upadhyaya, S.: Self-healing systems - survey and synthesis. Decission Support Systems 42(4), 2164–2185 (2007)
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)
Halima, R.B., Drira, K., Jmaiel, M.: A QoS-Oriented Reconfigurable Middleware for Self-Healing Web Services. In: ICWS, pp. 104–111 (2008)
Hinchey, M.G., Sterritt, R., Rouff, C.A.: Swarms and swarm intelligence. IEEE Computer 40(4), 111–113 (2007)
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)
Huebscher, M.C., McCann, J.A.: A survey of autonomic computing—degrees, models, and applications. ACM Comput. Surv. 40(3), 1–28 (2008)
Jennings, N.R.: Building complex, distributed systems: the case for an agent-based approach. Comms. of the ACM 44(4), 35–41 (2001)
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)
Kephart, J.O., Chess, D.M.: The vision of autonomic computing. IEEE Computer 36(1), 41–50 (2003)
Kirkpatrick, S., Gelatt Jr., D., Vecchi, M.P.: Optimization by simmulated annealing. Science 220(4598), 671–680 (1983)
Lee, C., Suzuki, J.: An immunologically-inspired autonomic framework for self-organizing and evolvable network applications. TAAS 4(4) (2009)
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)
Mogul, J.C.: Emergent (mis)behavior vs. complex software systems. Technical Report HPL-2006-2, HP Laboratories Palo Alto (2005)
Olariu, S., Zomaya, A.Y. (eds.): Handbook of Bioinspired Algorithms and Applications. CRC Press, Boca Raton (2005)
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)
Pierce, W.H.: Failure-tolerant Computer Design. Academic Press, London (1965)
Prokopenko, M.: Design vs. Self-organization. In: Prokopenko, M. (ed.) Advances in Applied Self-organizing Systems, pp. 3–17. Springer, London (2008)
Psaier, H., Dustdar, S.: A survey on self-healing systems - approaches and systems. Computing 87(1) (2010)
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)
Salehie, M., Tahvildari, L.: Self-adaptive software: Landscape and research challenges. ACM Trans. Auton. Adapt. Syst. 4(2), 1–42 (2009)
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)
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)
Serugendo, G.D.M., Gleizes, M.P., Karageorgos, A.: Self-organisation and emergence in mas: An overview. Informatica 30, 45–54 (2006)
Di Marzo Serugendo, G., Fitzgerald, J.: Designing and controlling trustworthy self-organising systems. Perada Magazine (2009)
Shapiro, M.W.: Self-healing in modern operating systems. ACM Queue 2(9), 66–75 (2005)
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)
Sterritt, R.: Autonomic computing. Innovations in Systems and Software Engineering 1(1), 79–88 (2005)
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)
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)
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)
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)
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)
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)
Taheri, J., Zomaya, A.Y.: A simulated annealing approach for mobile location management. Computer Communications 30(4), 714–730 (2007)
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)
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)
Viroli, M., Zambonelli, F.: A biochemical approach to adaptive service ecosystems. Inform. Sci. (2009)
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)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
André, F. et al. (2010). Architectures & Infrastructure. In: Papazoglou, M.P., Pohl, K., Parkin, M., Metzger, A. (eds) Service Research Challenges and Solutions for the Future Internet. Lecture Notes in Computer Science, vol 6500. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17599-2_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-17599-2_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17598-5
Online ISBN: 978-3-642-17599-2
eBook Packages: Computer ScienceComputer Science (R0)