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M. Vögler:
"SILCA - self-initiative load clustering agents";
Betreuer/in(nen): E. Kühn; E185-1, 2011.

To handle the load of a service more efficiently load clustering can be used to cluster a set of loads into smaller subsets, where each of this so called clusters captures a specific aspect of the load. There exist many clustering algorithms reaching from unintelligent to intelligent ones. However most approaches are very problem oriented and therefore hard to compare. In this thesis we propose a generic architectural pattern for a load clustering framework that allows the plugging of different clustering and classification algorithms.
Furthermore this pattern should ease the selection of the best algorithm for a certain problem scenario. The presented pattern assumes autonomous agents and a blackboard based communication mechanism to achieve a high software architecture agility. The pattern can be composed towards more complex network topologies which supports the combination of different algorithms. To proof the concept, the pattern and several clustering algorithms have been implemented and benchmarked, as part of this thesis.