Evaluating content management techniques for Web proxy caches
ACM SIGMETRICS Performance Evaluation Review
Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks
Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks
IEEE Internet Computing
IEEE Transactions on Knowledge and Data Engineering
A survey of Web cache replacement strategies
ACM Computing Surveys (CSUR)
Web proxy cache replacement scheme based on back-propagation neural network
Journal of Systems and Software
A quantitative study of recency and frequency based web cache replacement strategies
Proceedings of the 11th communications and networking simulation symposium
Training of NNPCR-2: an improved neural network proxy cache replacement strategy
SPECTS'09 Proceedings of the 12th international conference on Symposium on Performance Evaluation of Computer & Telecommunication Systems
An adaptive neural network-based method for tile replacement in a web map cache
ICCSA'11 Proceedings of the 2011 international conference on Computational science and its applications - Volume Part I
Journal of Network and Computer Applications
Intelligent Naïve Bayes-based approaches for Web proxy caching
Knowledge-Based Systems
Intelligent Web proxy caching approaches based on machine learning techniques
Decision Support Systems
GPC'12 Proceedings of the 7th international conference on Advances in Grid and Pervasive Computing
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As the Internet has become a more central aspect for information technology, so have concerns with supplying enough bandwidth and serving web requests to end-users in an appropriate time frame. Web caching help decrease bandwidth, lessen user perceived lag, and reduce loads on origin servers by storing copies of web objects on servers closer to end users as opposed to forwarding all requests to the origin servers. Since web caches have limited space, web caches must effectively decide which objects are worth caching or replacing for other objects. This problem is known as cache replacement. We used neural networks to solve this problem and proposed the Neural Network Proxy Cache Replacement (NNPCR) method. In this paper we propose an improved strategy of NNPCR referred to as NNPCR-2. We implemented NNPCR-2 in Squid proxy server and compared it with four other cache replacement strategies. In this paper we use 84 times more data than NNPCR was tested against and present exhaustive test results for NNPCR-2 with different trace files and neural network structures. Our results demonstrate that NNPCR-2 made important, balanced decisions in relation to the hit rate and byte hit rate; the two performance metrics most commonly used to measure the performance of web proxy caches.