A proposed framework for cloud-aware multimodal multimedia big data analysis toward optimal resource allocation

Abstract

Abstract The main goal of this paper is to demonstrate the structural design of Multimodal Multimedia Services in Cloud Platform (MMSCP). Thus, our proposed MMSCP architecture is built of three levels such as, Service Platform, Execution Platform and Structural platform. The functionality of service platform is to gather different forms of video files generated by the media creators and to store these files on the local platform. The second execution platform integrates both the Hadoop and Mapreduce functionalities. Finally, QoS based cloud computing functionalities (i.e. load balancing, security, resource allocation and network traffic management) is employed at the third structural platform. Likely, we introduced the Crow Search Algorithm (CSA) in structural platform for optimal allocation of resources. We adapt a Hadoop cluster to perform the experiment. Also, to conduct the resource allocation experiment we used some of the conventional optimization algorithms such as, ABC, GA and PSO for comparison with our proposed CSA algorithm imposed on the structural platform. However, to evaluate the performance of the algorithms we configured the CloudAnalyst tool. The simulation results illustrate that the proposed algorithm can allocate the virtual machine (VM) optimally to attain a minimal response time.