Experimenting Cloud Infrastructure for Tomorrows Big Data Analytics
Amitkumar S. Manekar1, G. Pradeepini2

1Mr. Amitkumar Manekar, Research Scholar, Department of CSE, KLEF, Vijayawada (Andhra Pradesh), India.
2Dr. G. Pradeepini, Professor, Department of CSE, KLEF, Vijayawada (Andhra Pradesh), India.
Manuscript received on 07 March 2019 | Revised Manuscript received on 20 March 2019 | Manuscript published on 30 March 2019 | PP: 885-890 | Volume-8 Issue-5, March 2019 | Retrieval Number: E3218038519/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Agile Cloud computing is today’s need. Most of the business and enterprises already acquire cloud storage as their mainstream solution for data processing. These mainstream solution are already struggling with limited infrastructure of cloud computing. In day to day business processes large amount of data is generated and these data are migrated on cloud at the end of day. Today’s cloud enables data processing, storage and distribution, system are not competent for moving large amounts of data in and out of the cloud. This actually is nothing but challenge for organizations with terabytes of digital content and managing cloud infrastructure for their daily real-time data crunching operation on demand. A high speed transport solution is required for transforming agility of on demand cloud infrastructure and merging big data analytics. Researchers need to developed such kind of mechanism where progressive data will be transferred on cloud with limited infrastructure of cloud computing. Today data migration is still carried out by managing hardware and dumping data on that hardware. No online solution and mechanism which can take care of this progressive data is available. Traditional hardware moving approach i.e. dumping data in hard drive or copy data in to magnetic tape and then transfer this data to cloud enable environment manually have many challenges associated it. In this paper we tried to explore opportunity of migrating big data to cloud infrastructure in optimized way. First part of this research work is discussing traditional data migration techniques and second part is experimenting these traditional data migration techniques on simulation based environment with ORTm algorithm which is modified version of ORT (Optimal Response Time). Here in ORTm we have focused on two parameter basically completion time of the last task and maximum resource utilization.
Keyword: Cloud, Data Migration, Migration Algorithm’s, ORTm, Big Data Analytics.
Scope of the Article: Cloud Computing and Networking