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An Efficient Data Integration Framework in Cloud Using MapReduce

  • P. Srinivasa RaoEmail author
  • M. H. M. Krishna Prasad
  • K. Thammi Reddy
Chapter
  • 939 Downloads
Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSAPPLSCIENCES)

Abstract

In Bigdata applications, providing security to massive data is an important challenge because working with such data requires large scale resources that must be provided by cloud service provider. Here, this paper demonstrates a cloud implementation and technologies using big data and discusses how to protect such data using hashing and how users can be authenticated. In particular, technologies using big data such as the Hadoop project of Apache are discussed, which provides parallelized and distributed data analyzing and processing of petabyte of data, along with a summarized view of monitoring and usage of Hadoop cluster. In this paper, an algorithm called FNV hashing is introduced to provide integrity of the data that has been outsourced to cloud by the user. The data within Hadoop cluster can be accessed and verified using hashing. This approach brings out to enable many new security challenges over the cloud environment using Hadoop distributed file system. The performance of the cluster can be monitored by using ganglia monitoring tool. This paper designs an evaluation cloud model which will provide quantity related results for regularly checking accuracy and cost. From the results of the experiment found out that this model is more accurate, cheaper and can respond in real time.

Keywords

Big data Hadoop MapReduce Cloud computing Accuracy Consumption 

References

  1. 1.
  2. 2.
  3. 3.
    Svantesson D, Clarke R (2010) Privacy and consumer risks in cloud computing. Comput Law Secur Review 26(4):391–397CrossRefGoogle Scholar
  4. 4.
    King NJ, Raja VT (2012) Protecting the privacy and security of sensitive customer data in the cloud. Comput Law Secur Rev 28(3):308–319CrossRefGoogle Scholar
  5. 5.
    Breitinger F, Stivaktakis G, Baier H (2013) A framework to test algorithms of similarity hashing. Digit Invest 10:S50–S58CrossRefGoogle Scholar
  6. 6.
    Rupesh M, Chitre DK (2012) Data leakage and detection of guilty agent. Int J Sci Eng Res 3(6)Google Scholar
  7. 7.
  8. 8.
  9. 9.
    Zhao J, Wang L, Tao J, Chen J, Sun W, Ranjan R, Kołodziej J, Streit A, Georgakopoulos D (2014) A security framework in GHadoop for bigdata computing across distributed Cloud data centers. Comput Syst Sci 80:994–1007CrossRefzbMATHGoogle Scholar
  10. 10.
    Wang L, Tao J, Ranjan R, Marten H, Streit A, Chen D, Chen J (2013) G-Hadoop: mapreduce across distributed data centers from data-intensive computing. Future Gener Comput Syst 29(3):739CrossRefGoogle Scholar
  11. 11.
    Caballer M, de Alfonso C, Molto G, Romero E, Blanquer I, Garcia A (2014) Code cloud: A platform to enable execution of programming models on the Clouds. J Syst Softw 93:187–198CrossRefGoogle Scholar
  12. 12.
    AL-Saiyd NA, Sail N (2013) Data integrity in cloud computing security. Theor Appl Inform Technol 58Google Scholar
  13. 13.
    Dillibabu M, Kumari S, Saranya T, Preethi R (2013) Assured protection and veracity for cloud data using Merkle hash tree algorithm. Indian J Appl Res 3:1–3Google Scholar
  14. 14.
    Mounika CH, RamaDevi L, Nikhila P (2013) Sample load rebalancing for distributed hash table in cloud. ISRO J Comput Eng 13:60–65CrossRefGoogle Scholar

Copyright information

© The Author(s) 2015

Authors and Affiliations

  • P. Srinivasa Rao
    • 1
    Email author
  • M. H. M. Krishna Prasad
    • 2
  • K. Thammi Reddy
    • 3
  1. 1.Department of Computer Science EngineeringMVGR College of EngineeringVizianagaramIndia
  2. 2.Department of Computer Science EngineeringJawaharlal Nehru Technological UniversityKakinadaIndia
  3. 3.Department of Computer Science EngineeringGITAM UniversityVisakhapatnamIndia

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