Efficient multimedia content storage and allocation in multidimensional cloud computing resources

Abstract

Optimal management of the cloud resources for multimedia contents is the important aim of this research. In our previous work, multiple kernel learning with support vector machine (MKL-SVM) is introduced, which can achieve a balanced resource usage with multimedia user request. However, existing work do not concentrate on caching mechanism which might lead to more computational overhead. To solve this problem, new method is proposed namely improved storage and scheduling of multimedia contents in cloud storage (ISS-MCCS). In this work, fuzzy neural network classification is utilised for handling the server clusters with unevenness. Then task scheduling is done using hybrid genetic-cuckoo search algorithm where hybrid fuzzy weighting scheme is used for the fitness evaluation. Finally, adaptive replacement cache (ARC) is integrated to optimise memory. The overall assessment of the research work is done in cloudsim environment which proves it can manage the multimedia contents with efficiently.