Contributions to Books:

V. Sesum-Cavic, E. Kühn:
"Self-Organized Load Balancing through Swarm Intelligence";
in: "Next Generation Data Technologies for Collective Computational Intelligence", N. Bessis, F. Xhafa (ed.); Springer-Verlag, 2011, ISBN: 978-3-642-20343-5, 195 - 224.

English 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.

"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)

Created from the Publication Database of the Vienna University of Technology.