Multilayer feedforward networks are universal approximators
Neural Networks
Web protocols and practice: HTTP/1.1, Networking protocols, caching, and traffic measurement
Web protocols and practice: HTTP/1.1, Networking protocols, caching, and traffic measurement
An Adaptive Web Cache Access Predictor Using Neural Network
IEA/AIE '02 Proceedings of the 15th international conference on Industrial and engineering applications of artificial intelligence and expert systems: developments in applied artificial intelligence
Analysis of a Least Recently Used Cache Management Policy for Web Browsers
Operations Research
Cache Pollution in Web Proxy Servers
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
A survey of Web cache replacement strategies
ACM Computing Surveys (CSUR)
Web cache optimization with nonlinear model using object features
Computer Networks: The International Journal of Computer and Telecommunications Networking
Exploiting client caches to build large Web caches
The Journal of Supercomputing
Analyzing Document-Duplication Effects on Policies for Browser and Proxy Caching
INFORMS Journal on Computing
Engineering Applications of Artificial Intelligence
Web proxy cache replacement scheme based on back-propagation neural network
Journal of Systems and Software
A new approach for a proxy-level web caching mechanism
Decision Support Systems
Intelligent Naïve Bayes-based approaches for Web proxy caching
Knowledge-Based Systems
Hi-index | 12.05 |
This paper proposes a novel contribution in Web caching area, especially in Web cache replacement, so-called intelligent client-side Web caching scheme (ICWCS). This approach is developed by splitting the client-side cache into two caches: short-term cache that receives the Web objects from the Internet directly, and long-term cache that receives the Web objects from the short-term cache. The objects in short-term cache are removed by least recently used (LRU) algorithm as short-term cache is full. More significantly, when the long-term cache saturates, the neuro-fuzzy system is employed efficiently in managing contents of the long-term cache. The proposed solution is validated by implementing trace-driven simulation and the results are compared with least recently used (LRU) and least frequently used (LFU) algorithms; the most common policies of evaluating Web caching performance. The simulation results have revealed that the proposed approach improves the performance of Web caching in terms of hit ratio (HR), up to 14.8% and 17.9% over LRU and LFU. In terms of byte hit ratio (BHR), the Web caching performance is improved up to 2.57% and 26.25%, and for latency saving ratio (LSR), the performance is better with 8.3% and 18.9% over LRU and LFU, respectively.