T. Cowsalya, S. Senthamarai Kannan, SVS College of Engineering, Coimbatore, Tamilnadu, India.
DOI : 01.0401/ijaict.2014.07.27
International Journal of Advanced Information and Communication Technology
Received On : January 05, 2017
Revised On : February 15, 2017
Accepted On : March 09, 2017
Published On : April 05, 2017
Volume 04, Issue 04
Pages : 638-642
Abstract
Information is increasingly important in our daily lives. We need information when and where it is required. We access the internet everyday to perform searches, to participate in social network, send and receive email, share pictures and videos and other applications. Therefore cost minimization for processing data has become an important issue in big data era. Here considered three factors like Data assignment, data placement and data movement which manipulate the operational expenses of data centers. In many scenarios, data are, however, geographically distributed across multiple data centers, and easily moving all data to a single data center before processing it can be expensive. We evaluate possible ways of executing such jobs, and propose Two Dimensional Markov Chain Models that can be used to resolve schedules for job sequences which are optimized either with respect to execution time or financial cost.
Keywords
Cost Minimization, Mapreduce, Data Centers.
Cite this article
T. Cowsalya, S. Senthamarai Kannan, “Cost Minimization of Running Mapreduce across Geographically Distributed Data Centers” INTERNATIONAL JOURNAL OF ADVANCED INFORMATION AND COMMUNICATION TECHNOLOGY, pp.638-642, April 05, 2017.
Copyright
© 2017 T. Cowsalya, S. Senthamarai Kannan. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.