Publication Type:

Journal Article


Evolving Systems, Springer Berlin/Heidelberg, Volume 2, Number 2, p.101–118 (2010)


Web caches are useful in reducing the user perceived latencies and web traffic congestion. Multi-level classification of web objects in caching is relatively an unexplored area. This paper proposes a novel classification scheme for web cache objects which utilizes a multinomial logistic regression (MLR) technique. The MLR model is trained to classify web objects using the information extracted from web logs. We introduce a novel grading parameter worthiness as a key for the object classification. Simulations are carried out with the datasets generated from real world trace files using the classifier in Least Recently Used-Class Based (LRU-C) and Least Recently Used-Multilevel Classes (LRU-M) cache models. Test results confirm that the proposed model has good online learning and prediction capability and suggest that the proposed approach is applicable to adaptive caching.

Cite this Research Publication

G. P. Sajeev and Sebastian, M. P., “A novel content classification scheme for web caches”, Evolving Systems, vol. 2, pp. 101–118, 2010.