Abstract
The load balancing problem is ubiquitous in information technologies. New technologies develop rapidly and their complexity becomes a critical issue. One proven way to deal with increased complexity is to employ a self-organizing approach. There are many different approaches that treat the load balancing problem but most of them are problem specific oriented and it is therefore difficult to compare them. We constructed and implemented a generic architectural pattern, called SILBA, which stands for “self-initiative load balancing agents”. It allows for the exchanging of different algorithms (both intelligent and unintelligent ones) through plugging. In addition, different algorithms can be tested in combination at different levels. The goal is to ease the selection of the best algorithm(s) for a certain problem scenario. SILBA is problem and domain independent, and can be composed towards arbitrary network topologies. The underlying technologies encompass a black-board based communication mechanism, autonomous agents and decentralized control. In this chapter, we present the complete SILBA architecture by putting the accent on using SILBA at different levels, e.g., for load balancing between agents on one single node, on nodes in one subnet, and between different subnets. Different types of algorithms are employed at different levels. Although SILBA possesses self-organizing properties by itself, a significant contribution to self-organization is given by the application of swarm based algorithms, especially bee algorithms that are modified, adapted and applied for the first time in solving the load balancing problem. Benchmarks are carried out with different algorithms and in combination with different levels, and prove the feasibility of swarm intelligence approaches, especially of bee intelligence.
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References
Androutsellis-Theotokis, S., Spinellis, D.: A survey of peer-to-peer content distribution technologies. ACM Computing Surveys 36(4), 335–371 (2004)
Bronevich, A.G., Meyer, W.: Load balancing algorithms based on gradient methods and their analysis through algebraic graph theory. Journal of Parallel and Distributed Computing 68(2), 209–220 (2008)
Cabri, G., Leonardi, L., Zambonelli, F.: Mars: A programmable coordination architecture for mobile agents. IEEE Internet Computing 4(4), 26–35 (2000)
Camazine, S., Sneyd, J.: A model of collective nectar source selection by honey bees: Self-organization through simple rules. Journal of Theoretical Biology 149, 547–571 (1991)
Chen, J.C., Liao, G.X., Hsie, J.S., Liao, C.H.: A study of the contribution made by evolutionary learning on dynamic load-balancing problems in distributed computing systems. Expert Systems with Applications 34(1), 357–365 (2008)
Chong, C.S., Sivakumar, A.I., Low, M.Y., Gay, K.L.: A bee colony optimization algorithm to job shop scheduling. In: Proceedings of the Thirty-Eight Conference on Winter Simulation, pp. 1954–1961 (2006)
Cortes, A., Ripolli, A., Cedo, F., Senar, M.A., Luque, E.: An asynchronous and iterative load balancing algorithm for discrete load model. Journal of Parallel and Distributed Computing 62(12), 1729–1746 (2002)
Da Silva, D.P., Cirne, W., Brasileiro, F.V.: Trading Cycles for Information: Using Replication to Schedule Bag-of-Tasks, pp. 169–180. Applications on Computational Grids, Proceeding of European Conference on Parallel Processing (2003)
Di Caro, G., Dorigo, M.: AntNet: Distributed Stigmergetic Control for Communications Networks. Journal of Artificial Intelligence Research 9, 317–365 (1998)
Dorigo, M., Stuetzle, T.: Ant Colony Optimization. MIT Press, Cambridge (2005)
Gelernter, D., Carriero, N.: Coordination languages and their significance. ACM Communication 35(2), 97–107 (1992)
Ho, C., Ewe, H.: Ant colony optimization approaches for the dynamic load-balanced clustering problem in ad hoc networks. In: Proceeding of Swarm Intelligence Symposium, IEEE/SIS 2007, pp. 76–83 (2007)
Janssens, N., Steegmans, E., Holvoet, T., Verbaeten, P.: An agent design method promoting separation between computation and coordination. In: Proceedings of the 2004 ACM Symposium on Applied Computing, SAC 2004, pp. 456–461 (2004)
Jogalekar, P., Woodside, C.M.: Evaluating the Scalability of Distributed Systems. IEEE Transanctions on Parallel and Distributed Systems 11(6), 589–603 (2000)
Kühn, E., Mordinyi, R., Schreiber, C.: An extensible space-based coordination approach for modelling complex patterns in large systems. In: Proceedings of the Third International Symposium on Leveraging Applications of Formal Methods, pp. 634–648 (2008)
Kühn, E., Riemer, J., Lechner, L.: Integration of XVSM spaces with the Web to meet the challenging interaction demands in pervasive scenarios. Ubiquitous Computing and Communication Journal - Special issue of Coordination in Pervasive Environments 3 (2008)
Kühn, E., Mordinyi, R., Keszthelyi, L., Schreiber, C.: Introducing the Concept of Customizable Structured Spaces for Agent Coordination in the Production Automation Domain. In: Proceedings of the Eighth International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2009, pp. 625–632 (2009)
Kühn, E., Mordinyi, R., Lang, M., Selimovic, A.: Towards Zero-delay Recovery of Agents in Production Automation Systems. In: Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, vol. 2, pp. 307–310 (2009)
Kühn, E., Sesum-Cavic, V.: A space-based generic pattern for self-initiative load balancing agents. In: Aldewereld, H., Dignum, V., Picard, G. (eds.) ESAW 2009. LNCS (LNAI), vol. 5881, pp. 17–32. Springer, Heidelberg (2009)
Kühn, E.: Virtual Shared Memory for Distributed Architectures. Nova Science Publishers (2001)
Lemmens, N., De Jong, S., Tuyls, K., Nowé, A.: Bee behaviour in multi-agent systems. In: Tuyls, K., Nowe, A., Guessoum, Z., Kudenko, D. (eds.) ALAMAS 2005, ALAMAS 2006, and ALAMAS 2007. LNCS (LNAI), vol. 4865, pp. 145–156. Springer, Heidelberg (2008)
Lin, F.C., Keller, R.M.: The gradient model load balancing method. IEEE Transactions On Software Engineering 13(1), 32–38 (1987)
Markovic, G., Teodorovic, D., Acimovic-Raspopovic, V.: Routing and wavelength assignment in all-optical networks based on the bee colony optimization. AI Communications 20(4), 273–285 (2007)
Mordinyi, R., Kühn, E., Schatten, A.: Towards an Architectural Framework for Agile Software Development. In: Proceedings of the Seventeenth International Conference and Workshops on the Engineering of Computer-Based Systems, pp. 276–280 (2010)
Nakrani, S., Tovey, C.: On honey bees and dynamic server allocation in the Internet hosting centers. Adaptive Behaviour 12(3-4), 223–240 (2004)
Olague, G., Puente, C.: The Honeybee Search Algorithm for Three-Dimensional Reconstruction. In: Rothlauf, F., Branke, J., Cagnoni, S., Costa, E., Cotta, C., Drechsler, R., Lutton, E., Machado, P., Moore, J.H., Romero, J., Smith, G.D., Squillero, G., Takagi, H. (eds.) EvoWorkshops 2006. LNCS, vol. 3907, pp. 427–437. Springer, Heidelberg (2006)
Pham, D.T., Soroka, A.J., Ghanbarzadeh, A., Koç, E., Otri, S., Packianather, M.: Optimising neural networks for identification of wood defects using the Bees Algorithm. In: Proceedings of the IEEE International Conference on Industrial Informatics, pp. 1346–1351 (2006)
Pham, D.T., Koç, E., Lee, J.Y., Phrueksanant, J.: Using the Bees Algorithm to schedule jobs for a machine. In: Proceedings of the Eighth International Conference on Laser Metrology, pp. 430–439 (2007)
Picco, G.P., Balzarotti, D., Costa, P.: Lights: a lightweight, customizable tuple space supporting context-aware applications. In: Proceedings of the ACM Symposium on Applied Computing, SAC 2005, pp. 413–419 (2005)
Picco, G.P., Murphy, A.L., Roman, G.C.: Lime: Linda meets mobility. In: Proceedings of the IEEE International Conference on Software Engineering, pp. 368–377 (1999)
Šešum-Čavić, V., Kühn, E.: Instantiation of a generic model for load balancing with intelligent algorithms. In: Hummel, K.A., Sterbenz, J.P.G. (eds.) IWSOS 2008. LNCS, vol. 5343, pp. 311–317. Springer, Heidelberg (2008)
Šešum-Čavić, V., Kühn, E.: Comparing configurable parameters of Swarm Intelligence Algorithms for Dynamic Load Balancing. In: Proceedings of the Fourth IEEE International Conference on Self-Adaptive and Self-Organizing Systems, Workshop Self-Adaptive Network, SASO/SAN, pp. 255–256 (2010)
Shivaratri, N.G., Krueger, P.: Adaptive Location Policies for Global Scheduling. IEEE Transactions on Software Engineering 20, 432–444 (1994)
Shoham, Y., Leyton-Brown, K.: Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations. Cambridge University Press, Cambridge (2009)
Van Steen, M., Van der Zijden, S., Sips, H.J.: Software Engineering for Scalable Distributed Applications. In: Proceedings of the Twenty-Second International Computer Software and Applications Conference, COMPSAC, pp. 285–293 (1998)
Wong, L.P., Low, M.Y., Chong, C.S.: A Bee Colony Optimization for Traveling Salesman Problem. In: Proceedings of the Second Asia International Conference on Modelling & Simulation, AMS, pp. 818–823. IEEE, Los Alamitos (2008)
Yang, X.: Nature-Inspired Metaheuristic Algorithms. Luniver Press (2008)
Zhou, S.: A trace-driven simulation study of dynamic load balancing. IEEE Transactions on Software Engineering 14(9), 1327–1341 (1988)
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Šešum-Čavić, V., Kühn, E. (2011). Chapter 8 Self-Organized Load Balancing through Swarm Intelligence. In: Bessis, N., Xhafa, F. (eds) Next Generation Data Technologies for Collective Computational Intelligence. Studies in Computational Intelligence, vol 352. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20344-2_8
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DOI: https://doi.org/10.1007/978-3-642-20344-2_8
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