Fractal self-similarity measurements based clustering technique for SOAP Web messages

The significant increase in the usage of Web services has resulted in bottlenecks and congestion on bandwidth-constrained network links. Aggregating SOAP messages can be an effective solution that could potentially reduce the large amount of generated traffic. Although pairwise SOAP aggregation, that is grouping only two similar messages, has demonstrated significant performance improvement, additional improvements can be done by including similarity mechanisms. Such mechanisms cluster several SOAP messages that have high degree of similarity. This paper proposes a fractal self-similarity model that provides a novel way of computing the similarity of SOAP messages. Fractal is proposed as an unsupervised clustering technique that dynamically groups SOAP messages. Various experimentations have shown good performance results for the proposed fractal self-similarity model in comparison with some well-known clustering models by only consuming 31% of the clustering time required by the K-Means and 23% when using principle component analysis (PCA) combined with K-Means. Furthermore, the proposed technique has shown “better” quality clustering, as the aggregated SOAP messages have much smaller size than their counterparts.

► New unsupervised Fractal based clustering technique for SOAP messages.
► Higher performance in comparison with other well-known clustering models.
► Dynamic clustering that guarantees high similarity degree of SOAP messages.