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Storage and Processing Speed for Knowledge from Enhanced Cloud Computing With Hadoop Frame Work : A Survey

Authors(3):

SK. Jilani Basha, P. Anil Kumar, S. Giri Babu
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Cloud is a Pool of servers, all the servers are interconnected through internet, The main problem in cloud is retrieving of data (knowledge) and process that variety of data and here other problem is security for that data, Generally now a day’s different types of, I mean variety of data (Structured, semi-structured and Unstructured data) is existed in the different social applications (face book).So, and another problem with historical data retrieving. These types of problems are resolved with help of hadoop frame work and Sqoop and flume tools. Sqoop is load the data from database to Hadoop (HDFS), and flume loads the data from server files to hadoop distributed file system. Storage problem is resolving with help of blocks in hadoop distributed file system and processing is resolving with help of map reduce and pig and hive and spark etc. This paper summarizes the storage and processing speed in the enhanced cloud with hadoop framework.

SK. Jilani Basha, P. Anil Kumar, S. Giri Babu

Cloud Computing, Hadoop Frame Work, Infrastructure as a Service, Platform as a Service, Software as Service

  1. O. Vallis, J. Hochenbaum, A. Kejariwal. A Novel Technique for Long-term Anomaly Detection in the Cloud, Proceedings of the 6th USENIX Conference on Hot Topics in Cloud Computing (HotCloud 2014), Philadelphia, USA .
  2. K. Bhaduri, K. Das, B. L. Matthews. Detecting Abnormal Machine Characteristics in Cloud Infrastructures, Proceedings of the 11th International Conference on Data Mining Workshops (ICDMW 2011), Vancouver, Canada.
  3. Y. Tan, H. Nguyen, Z. Shen, X. Gu, C. Venkatramani, D. Rajan. PREPARE: Predictive Performance Anomaly Prevention for Virtualized Cloud Systems, Proceedings of the 32nd IEEE International Conference on Distributed Computing Systems (ICDCS 2012), Macau, China.
  4. S. Islam, J. Keung, K. Lee, A. Liu, Empirical prediction models for adaptive resource provisioning in the cloud. Future Generation Computer Systems 28(1):155-162, Elsevier, 2012.
  5. T. Lu, M. Stuart, K. Tang, X. He. Clique Migration: Affinity Grouping of Virtual Machines for Inter-Cloud Live Migration, Proceedings of the 9th IEEE International Conference on Networking, Architecture, and Storage (NAS 2014), Tianjin, China.
  6. R. Buyya, C. S. Yeo, and S. Venugopal, Market-Oriented Cloud Computing: Vision, Hype, and Reality for Delivering IT Services as Computing Utilities, Proceedings of the 10th IEEE International Conference on High Performance Computing and Communications (HPCC 2008), Dalian, China.
  7. R. N. Calheiros, R. Ranjan, and R. Buyya. Virtual Machine Provisioning Based on Analytical Performance and QoS in Cloud Computing Environments, Proceedings of the 40th International Conference on Parallel Processing (ICPP 2011), Taipei, Taiwan.
  8. Rajkumar Buyya, Kotagiri Ramamohanarao, Chris Leckie, Rodrigo N. Calheiros, Amir Vahid Dastjerdi1, and Steve Versteeg , Big Data Analytics-Enhanced Cloud Computing: Challenges, Architectural Elements, and Future Directions, conference paper in Proceedings of the 21st IEEE International Conference on Parallel and Distributed Systems (ICPADS 2015, IEEE Press, USA), Melbourne, Australia, December 14-17, 2015.

Publication Details

Published in : Volume 2 | Issue 2 | March-April - 2016
Date of Publication Print ISSN Online ISSN
2016-04-25 2395-1990 2394-4099
Page(s) Manuscript Number   Publisher
126-132 IJSRSET162236   Technoscience Academy

Cite This Article

SK. Jilani Basha, P. Anil Kumar, S. Giri Babu, "Storage and Processing Speed for Knowledge from Enhanced Cloud Computing With Hadoop Frame Work : A Survey", International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 2, Issue 2, pp.126-132, March-April-2016.
URL : http://ijsrset.com/IJSRSET162236.php