Ensemble Prefetching Through Classification Using Support Vector Machine
Owing to the steadfast growth of the Internet web objects and its multiple types, the latency incurred by the clients to retrieve a web document is perceived to be higher. Web prefetching is a challenging yet achievable technique to reduce the thus perceived latency. It anticipates the objects that may be requested in future based on certain features and fetches them into cache before actual request is made. Therefore, to achieve higher cache hit rate group prefetching is better. According to this, classification of web objects as groups using features like relative popularity and time of request is intended. Classification is aimed using Support Vector Machine learning approach and its higher classification rate reveals effective grouping. Once classified, prefetching is performed. Experiments are carried out to study the prefetching performance through Markov model, ART1, linear SVM and multiclass SVM approach. Compared to other techniques, a maximum hit rate of 93.39% and 94.11% with OAO and OAA SVM multiclass approach is attained respectively. Higher hit rate exhibited by the multiclass Support Vector Machine demonstrates the efficacy of the proposal.
KeywordsPrefetching Classification Machine learning SVM Hit rate ART1
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