A Comparative Study for Optimization of Video File Compression in Cloud Environment

• Published in: International journal of computer applications 2012-01, Vol.60 (13), p.27-30
• Main Authors: Chahal, Navdeep S, Khehra, Baljit S
• Format: Article
• Language: English
• Subjects: Compressing; Encoding; Fragments; Infrastructure; Internet; Lectures; Source code; Splitting
• ISSN: 0975-8887; 0975-8887
• DOI: 10.5120/9753-4334

Many organizations like hospitals for telemedicine, journalism for live-telecast and academias are using a service video-on-demand for delivering the lectures and research contents to the remote locations across the globe. The videos to be broadcasted are time and resource consuming due to the large amount of data and due to these constraints, for getting fast access over Internet and mobile devices, such video applications need to be compressed into another format. The usage of videos is occasional so to save huge infrastructure cost and time, the Infrastructure as a Service (IaaS) Cloud systems can be leveraged. In this paper, an attempt has been made to design, implement and optimize the performance of Digital Video to MPEG4 transcoding in the Cloud environment using Meghdoot (an Open-Source Cloud stack). The classical MapReduce approach is used to rationalize the use of resources by exploring on demand computing and performs parallel video conversion thereby reducing the video encoding times. Experimental results point out to suitability of better performance that by varying the technique of splitting the video file size of fragments that is through Mencoder and through default Hadoop Splitting. The comparison of both the systems to get the best compression times will help us to optimize the Cloud resources that further helps in trade-off between time, cost and quality.