Visible to the public IntellCache: An Intelligent Web Caching Scheme for Multimedia Contents

TitleIntellCache: An Intelligent Web Caching Scheme for Multimedia Contents
Publication TypeConference Paper
Year of Publication2020
AuthorsNiloy, Nishat Tasnim, Islam, Md. Shariful
Conference Name2020 Joint 9th International Conference on Informatics, Electronics Vision (ICIEV) and 2020 4th International Conference on Imaging, Vision Pattern Recognition (icIVPR)
KeywordsCache Decision, Content Popularity, Deep Learning, Intelligent Web Caching, Load modeling, metadata, Metrics, Micromechanical devices, Prediction algorithms, Predictive models, Proactive Caching, pubcrawl, Resiliency, Scalability, Servers, Web Caching
AbstractThe traditional reactive web caching system is getting less popular day by day due to its inefficiency in handling the overwhelming requests for multimedia content. An intelligent web caching system intends to take optimal cache decisions by predicting future popular contents (FPC) proactively. In recent years, a few approaches have proposed some intelligent caching system where they were concerned about proactive caching. Those works intensified the importance of FPC prediction using the prediction models. However, only FPC prediction may not help to get the optimal solution in every scenario. In this paper, a technique named IntellCache has been proposed that increases the caching efficiency by taking a cache decision i.e. content storing decision before storing the predicted FPC. Different deep learning models such as- multilayer perceptron (MLP), Long short-term memory (LSTM) of Recurrent Neural Network (RNN) and ConvLSTM a combination of LSTM and Convolutional Neural Network (CNN) are compared to identify the most efficient model for FPC. The information on the contents of 18 years from the MovieLens data repository has been mined to evaluate the proposed approach. Results show that this proposed scheme outperforms previous solutions by achieving a higher cache hit ratio and lower average delay and thus, ensures users' satisfaction.
DOI10.1109/ICIEVicIVPR48672.2020.9306604
Citation Keyniloy_intellcache_2020