Abstract   Cloud computing is the one of the emerging techniqu es to process the big data. Cloud computing is also, known as  service  on  demand.  Large  set  or  large  volume  of  dat a  is  known  as  big  data.  Processing  big  data  (MRI  im ages  and  DICOM  images)  normally  takes  more  time.  Hard  tasks  such  a s  handling  big  data  can  be  solved  by  using  the  concepts  of  hadoop.  Enhancing  the  hadoop  concept  will  help  the  user  to  process  the  large  set  of  images.  The  Hadoop  Distributed  File  System  (HDFS) and Map Reduce are the two default main func tions which is used to enhance hadoop.  HDFS is a hadoop file storing  system,  which  is  used  for  storing  and  retrieving  th e  data.  Map  Reduce  is  the  combination  of  two  functi ons  namely  maps  and  reduces. Map is the process of splitting the inputs  and reduce is the process of integrating the outpu t of map’s input. Recently,  medical  experts  experienced  problems  like  machine  f ailure  and  fault  tolerance  while  processing  the  result  for  the  scanned  data.  A  unique  optimized  time  scheduling  algorithm,   called  Dynamic  Handover  Reduce  Function  (DHRF)  alg orithm  is  introduced  in  the  reduce  function.  Enhancement  of  h adoop  and  cloud  and  introduction  of  DHRF  helps  to  o vercome  the  processing risks, to get optimized result with less  waiting time and reduction in error percentage of  the output image.   

References

[1] Gao K., Wang Q., and Xi L., Reduct Algorithm based Execution Times Prediction in Knowledge Discovery Cloud Computing Environment, the International Arab Journal of Information Technology , vol .11, no. 3, pp. 268-275, 2013.

[2] Hao Y., Fast Corner Detection-machine Learning for High Speed Corner Detection, available at: http://www.docin.com.cn/p- 1429412129.html, last visited 2010.

[3] Jung G., Gnanasambandam N., Mukherjee T., Synchronous Parallel Processing of Big-data Analytics Services to Optimize Performance in Federated Clouds, Cloud Computing, in Proceedings of the 5 th International Conference on Digital Object Identifier , Honolulu, pp. 811- 818, 2012.

[4] Kalagiakos P. and Karampelas P., Cloud Computing Learning, in Proceedings of the 5 th International Conference Application of Information and Communication Technologies , Baku, pp. 1-4, 2011.

[5] Lee H., Kim M., Her J., and Lee H., Implementation of Mapreduce-based Image Conversion Module in Cloud Computing Environment, in Proceedings of the International Conference on Information Network , Bali, pp. 234-238, 2012 .

[6] Malik S., Huet F., and Caromel D., Cooperative Cloud Computing in Research and Academic Environment using Virtual Cloud, Emerging Technologies (ICET), in Proceedings of the International Conference on Digital Object Identifier , Islamabad, pp. 1-7, 2012.

[7] Patel A., Birla M., and Nair U., Addressing Big Data Problem using Hadoop and Map Reduce, Engineering (NUiCONE), in Proceedings of Nirma University International Conference on Engineering , Ahmedabad, pp. 1-5, 2012.

[8] Sood K., A Combined Approach to Ensure Data Security in Cloud Computing, the Journal of Network and Computer Applications , vol. 35, pp. 1831-1838, 2012.

[9] Srirama S., Jakovits P., and Vainikko E., Adapting Scientific Computing Problems to Clouds using Mapreduce, Future Generation Computer Systems , vol. 28, no.1, pp. 184-192, 2012.

[10] Wang L., Tao J., Ranjan R., Marten H., Streit A., Chen J., Chen D., and G-Hadoop, Mapreduce Across Distributed Data Centers for Data- Intensive Computing, Future Generation Computer Systems , vol. 29, no. 3, pp. 739-750, 2013.

[11] Wu T., Chen C., Kuo L., Lee W., and Chao H., Cloud-Based Image Processing System with Priority-based Data Distribution Mechanism, Computer Communications , vol. 35, no. 15, pp. 1809-1818, 2012. Mehraj Ali is a Full-Time PhD Research Scholar at Anna University, India. He has completed his MSc degree at Anna University, India. His Research areas includes cloud computing, big data, image processing etc., he has published articles in various National/International Conferen ces in his domain. He has also, published articles in p eer reviewed Journals. John Kumar obtained his PhD degree in Computer Applications from Anna University, India in 2010. He has started his teaching profession in the year 2003 to serve his parent Institution Thiagarajar College of Engineering, Madurai, where he obtained his Master Degree in Computer Applications. He has published several research pap ers in International and National Journals as well as conferences. His field of interest includes spatial data mining, cloud computing and image processing.