Abstract
In peer-to-peer networks, an important issue is the distribution of load having an impact on the overall performance of the system. The answer could be the application of an intelligent approach that leads to autonomic self-organizing infrastructures. In this position paper, we briefly introduce a framework model for load balancing that allows various load-balancing algorithms to be plugged-in, and that uses virtual shared-memory-based communication known to be advantageous for the communication of auto nomous agents in order to enable the collaboration of load-balancing agents. As the main contribution, we show how the biological concepts of bees can be mapped to the load-balancing problem, explain why we expect that bee intelligence can outperform other (un)intelligent approaches, and present an instantiation of the model with the bee intelligence algorithm. This load-balancing scheme focuses on two main policies: a transfer and a location policy for which we suggest some improvements.
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References
Androutsellis-Theotokis, S., Spinellis, D.: A survey of peer-to-peer content distribution technologies. ACM Comput. Surv. 36, 335–371 (2004)
Backschat, M., Pfaffinger, A., Zenger, C.: Economic-Based Dynamic Load Distribution in Large Workstation Networks. In: 2nd Int. Euro-Par Conf. on Parallel Processing, France, pp. 631–634 (1996)
Bronevich, A.G., Meyer, W.: Load-balancing algorithms based on gradient methods and their analysis through algebraic graph theory. Parallel and Distr. Comp. 68, 209–220 (2008)
Camazine, S., Sneyd, J.: A model of collective nectar source selection by honey bees: Self-organization through simple rules. J. of Theoretical Biology 149(4), 547–571 (1991)
Chen, J.C., Liao, G.X., Hsie, J.S., Liao, C.H.: A study of a contribution made by evolutionary learning on dynamic load-balancing problems in distributed computing systems. Expert Systems with Application 34, 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: Proc. of the 38th Conf. on Winter Simulation, California, pp. 1954–1961 (2006)
Cortes, A., Ripoll, A., Cedo, F., Senar, M.A., Luque, E.: An asynchronous and iterative LB algorithm for discrete load model. Parallel and Distr. Comp. 62, 1729–1746 (2002)
Da Silva, D.P., Cirne, W., Brasileiro, F.V., Grande, C.: Trading Cycles for Information: Using Replication to Schedule Bag-of-Tasks. In: Kosch, H., Böszörményi, L., Hellwagner, H. (eds.) Euro-Par 2003. LNCS, vol. 2790, pp. 169–180. Springer, Heidelberg (2003)
Dorigo, M., Di Caro, G., Gambardella, L.: Ant colony optimization: A new meta-heuristic. In: Proc. of the Congress on Evolutionary Computation, USA, vol. 2, pp. 1470–1477 (1999)
Eager, D.L., Lazowska, E.D., Zahorjan, J.: Adaptive Load Sharing in Homogeneous Distributed system. IEEE Trans. on Software Engineering 12(5), 662–675 (1986)
Grosu, D., Chronopoulos, A.T.: A Game-Theoretic Model and Algorithm for Load Balancing in Distributed Systems. In: APDCM 2002, USA, pp. 146–153 (2002)
Ho, C.K., Ewe, H.T.: Ant Colony Optimization Approaches for the Dynamic Load-Balanced Clustering Problem in Ad Hoc Networks. In: Swarm Intelligence Symp., Hawaii (2007)
Huang, Y., Garcia-Molina, H.: Publish/Subscribe in a Mobile Environment. In: 2nd Int. Workshop on Data Engineering for Wireless and Mobile Access, USA, pp. 27–34 (2001)
Kraus, K.: Development and Evaluation of a Load Balancer Based on Corso (in German), Praktikum, Institute for Computer Languages, TU Wien (2004)
Kühn, e.: Virtual Shared Memory for Distributed Architecture. Nova Science (2001)
Kühn, e., Mordinyi, R., Schreiber, C.: An Extensible Space-based Coordination Approach for Modeling Complex Patterns in Large Systems. In: Proc. 3rd Int. Symposium on Leveraging Applications of Formal Methods, Verification and Validation, Greece, October 13-15 (2008)
Kühn, e., Šešum-Cavic, V.: A Model for Self-Initiative Load Balancing Agents with Support for Swarm Intelligence and Genetic Algorithms (submitted for publication) (2008)
Lemmens, N., de Jong, S., Tuyls, K., Nowe, 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, vol. 4865, pp. 145–156. Springer, Heidelberg (2008)
Lin, F.C.H., Cellars, R.M.: The gradient of modelling Load-balancing Method. IEEE Trans. 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 Commun. 20(4), 273–285 (2007)
Nakrani, S., Tovey, C.: On honey bees and dynamic server allocation in the Internet hosting centers. Adaptive Behaviour 12(3-4), 223–240 (2004)
Pollak, R.: A Hierarchical Load Balancing Environment for Parallel and Distributed Supercomputer. In: Int. Symposium on Parallel and Distr. Supercomputing, Japan (1995)
Rodrigues, J.A.N., Monteiro, P.C.L., de Oliveira Sampaio, J., de Souza, J.M., Zimbrao, G.: Autonomic business processes scalable architecture. In: Business Process Management Workshops, pp. 78–83 (2007)
Rohner, M.: Load Balancing for Grid Computing (German), dipl. thesis, TU Wien (2005)
Shivaratri, N.G., Krueger, P.: Adaptive Location Policies for Global Scheduling. IEEE Trans. on Software Engineering 20(6), 432–444 (1994)
Wong, L.P., Low, M.Y.H., Chong, C.S.: A Bee Colony Optimization for Traveling Salesman Problem. In: 2nd Asia Int. Conf. on Modeling & Simulation, Malaysia, pp. 818–823 (2008)
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Sesum-Cavic, V., Kühn, e. (2008). Instantiation of a Generic Model for Load Balancing with Intelligent Algorithms. In: Hummel, K.A., Sterbenz, J.P.G. (eds) Self-Organizing Systems. IWSOS 2008. Lecture Notes in Computer Science, vol 5343. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92157-8_31
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DOI: https://doi.org/10.1007/978-3-540-92157-8_31
Publisher Name: Springer, Berlin, Heidelberg
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