Multi-modal Multimedia Big Data Analyzing Architecture and Resource Allocation on Cloud Platform

• Published in: Neurocomputing (Amsterdam) Vol. 253; pp. 135 - 143
• Main Authors: Jayasena, K.P.N., Li, Lin, Xie, Qing
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 30-08-2017
• Subjects: ACO; Cloud computing; Hadoop & MapReduce; Multi-modal; Multimedia big data; Resource allocation; Multimedia big data; Cloud computing; Multi-modal; ACO; Resource allocation; Hadoop & MapReduce
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2016.11.077

Multimedia big data analyzing is the new topic that focus on all features of distributed computing systems that contains of a combination of text, visual and audio modalities. The traditional method to transcoding multi-modal multimedia big data needs expensive hardware and the amount of data increases transcoding executes a significant burden on the computing infrastructure. Therefore we illustrate a novel implementation for multimedia big data analyzing and data distribution. Our proposed architecture contains three layers such as service layer, platform layer and infrastructure layer. We design and implement the platform layer of the system by using a MapReduce framework running on a hadoop distributed file system (HDFS) and the media processing libraries Xuggler. In this way, our proposed system reduces the time for transcoding large amounts of data into specific formats depending on the user requirements. It provides flexible multimedia record/write interface and we can build large scale multimedia big data analytic applications based on Hadoop cloud platform. Moreover, we proposed the ant colony optimization (ACO) algorithm for efficient resource allocation in infrastructure layer. The simulation results demonstrate that the proposed algorithm can optimally allocate VM to achieve a minimal response time.