Waleed Ali

Intelligent Client-Side Web Caching Scheme Based on Least Recently Used Algorithm and Neuro-Fuzzy System

Web caching is a well-known strategy for improving performance of Web-based system by keeping web objects that are likely to be used in the near future close to the client. Most of the current Web browsers still employ traditional caching policies that are not efficient in web caching. This research proposes a splitting client-side web cache to two caches, short-term cache and long-term cache. Primarily, a web object is stored in short-term cache, and the web objects that are visited more than the pre-specified threshold value will be moved to long-term cache, while other objects are removed by Least Recently Used(LRU) algorithm as short-term cache is full. More significantly, when the long-term cache saturates, the trained neuro-fuzzy system is employed in classifying each object stored in long-term cache into cacheable or uncacheable object. The old uncacheable objects are candidate for removing from the long-term cache. By implementing this mechanism, the cache pollution can be mitigated and the cache space can be utilized effectively. Experimental results have revealed that the proposed approach has better performance compared to the most common caching policies and has improved the performance of client-side caching substantially.

Intelligent Web proxy caching approaches based on machine learning techniques

In this paper, machine learning techniques are used to enhance the performances of conventional Web proxy caching policies such as Least-Recently-Used (LRU), Greedy-Dual-Size (GDS) and Greedy-Dual-Size-Frequency (GDSF). A support vector machine (SVM) and a decision tree (C4.5) are intelligently incorporated with conventional Web proxy caching techniques to form intelligent caching approaches known as SVM-LRU, SVM-GDSF and C4.5-GDS. The proposed intelligent approaches are evaluated by trace-driven simulation and compared with the most relevant Web proxy caching polices. Experimental results have revealed that the proposed SVM-LRU, SVM-GDSF and C4.5-GDS significantly improve the performances of LRU, GDSF and GDS respectively.

Intelligent Naïve Bayes-based approaches for Web proxy caching

Web proxy caching is one of the most successful solutions for improving the performance of Web-based systems. In Web proxy caching, the popular Web objects that are likely to be revisited in the near future are stored on the proxy server, which plays the key roles between users and Web sites in reducing the response time of user requests and saving the network bandwidth. However, the difficulty in determining the ideal Web objects that will be re-visited in the future is still a problem faced by existing conventional Web proxy caching techniques. In this paper, a Naive Bayes (NB) classifier is used to enhance the performance of conventional Web proxy caching approaches such as Least-Recently-Used (LRU) and Greedy-Dual-Size (GDS). NB is intelligently incorporated with conventional Web proxy caching techniques to form intelligent and effective caching approaches known as NB-GDS, NB-LRU and NB-DA. Experimental results have revealed that the proposed NB-GDS, NB-LRU and NB-DA significantly improve the performances of the existing Web proxy caching approaches across several proxy datasets.

Patterns analysis and classification for Web proxy cache

Web caching is one of the most successful solutions for improving the performance of Web-based systems. In Web proxy caching, the popular Web objects that are likely to be revisited in the near future are stored on the proxy server, which plays the key roles between users and Web sites in reducing the response time of user requests and saving the network bandwidth. However, the difficulty in determining which Web objects will be re-visited in the future is still a problem faced by existing Web proxy caching techniques. In this paper, machine learning techniques are implemented to cope with the above problem. We present new intelligent approaches, which depend on the capability of Support vector machine (SVM) and decision tree (C4.5) to learn from Web proxy logs file and predict the classes of objects to be re-visited or not. Experimental results have revealed that SVM and C4.5 produce very promising performance and much faster compared to both back-propagation neural network (BPNN) and neuro-fuzzy system (ANFIS).

Neuro-fuzzy system in web client-side caching

Web caching is a well-known strategy for improving performance of Web-based system by keeping web objects that are likely to be used in the near future closer to the client. Although most researchers focused on designing efficient caching with proxy and origin servers, the potential gain of exploiting client-side caching based on neuro-fuzzy system is not yet being investigated. Hence, this paper proposes a splitting web client-side cache to two caches, short-term cache and long-term cache. Initially, a web object is stored in short-term cache. The web objects that are visited more than the pre-specified threshold value will be moved to long-term cache and other objects in short-term cache are removed with time. Thus, we ensure that the preferred web objects are cached in long-term cache for longer time. In this study, neuro-fuzzy is employed to determine which web objects should be removed in order to create more spaces for the new web objects. By implementing this mechanism, the cache space is used properly. Experimental results have shown that the proposed approach has better performance compared to the most common caching policies and has improved the performance of client-side caching substantially.

Intelligent web proxy caching approaches based on support vector machine

Web proxy caching is one of the most successful solutions for improving the performance of Web-based systems. In Web proxy caching, the popular web objects that are likely to be revisited in the near future are stored on the proxy server which plays the key roles between users and web sites in reducing the response time of user requests and saving the network bandwidth. However, the difficulty in determining the ideal web objects that will be re-visited in the future is still a problem faced by existing conventional Web proxy caching techniques. In this paper, support vector machine (SVM) is used to enhance the performance of conventional web proxy caching such as Least-Recently-Used (LRU) and Greedy-Dual-Size-Frequency (GDSF). SVM is intelligently incorporated with conventional Web proxy caching techniques to form intelligent caching approaches called SVM_LRU and SVM_GDSF with better performance. Experimental results have revealed that the proposed SVM_LRU and SVM_GDSF improve significantly the performances of LRU and GDSF respectively across several proxy datasets.