A neural network proxy cache replacement strategy and its implementation in the Squid proxy server

Abstract

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 was introduced in the 1990s to help decrease network traffic, 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. The goal of this research is to implement NNPCR in a real environment like Squid proxy server. In order to do so, we propose an improved strategy of NNPCR referred to as NNPCR-2. We show how the improved model can be trained with up to twelve times more data and gain a 5-10% increase in Correct Classification Ratio (CCR) than NNPCR. 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.

Web Map Tile Services for Spatial Data Infrastructures: Management and Optimization

“…A survey on applications of neural networks and evolutionary techniques in web caching can be found in [27]. [28][29][30][31][32] proposes the use of a backpropagation neural network in a Web proxy cache for taking replacement decisions. A predictor that learns the patterns of Web pages and predicts the future accesses is presented in [33].…”

Multi-provider cloud computing network infrastructure optimization

“…NNPCR [54] and its extension, NNPCR-2 [55], apply artificial neural networks to rate object cacheability. The networks were supervised to learn cacheable patterns from valid HTTP status codes and object sizes.…”

Improving the Performance of a Proxy Cache Using Very Fast Decision Tree Classifier

“…Very Fast decision Tree learning method integrates with Greedy Dual Size Frequency 3,7,8 to improve the performance of Byte Hit Ratio. In this method, the VFDT uses as inputs the recency and frequency of the web object based on the sliding window mechanism, and as well as Retrieval time, the size of the Web object, and the classifier produces a target output in order to indicate whether the web object can visited in future or not see in Equation 5.…”