Improved Real-Time Data Elasticity on Stream Cloud

Improved Real-Time Data Elasticity on Stream Cloud

  IJCTT-book-cover
 		 International Journal of Computer Trends and Technology (IJCTT)          
 
© 2017 by IJCTT Journal
Volume-51 Number-1
Year of Publication : 2017
Authors : Mr. Chetan A. Joshi, Mr. Rohit N. Devikar
DOI :  10.14445/22312803/IJCTT-V51P104

MLA

Mr. Chetan A. Joshi, Mr. Rohit N. Devikar "Improved Real-Time Data Elasticity on Stream Cloud". International Journal of Computer Trends and Technology (IJCTT) V51(1):30-33, September 2017. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
Most of the applications in cloud domains such as online data processing, Fraud detection, large scale sensor network etc. where large amount of data should processed in real time. Earlier, for data stream processing, the centralized system environment was using with store and then process paradigms. After that some advancement has been introduced with distributed environment for data stream processing. Data Stream processing using novel computing paradigm which take query as input and splits that query into multiple sub queries and process the data on multiple sub clusters in such a way that reduces the distribution overheads. This kind of application generates very high input data which needs to process with the available clusters So High availability and elasticity are two key characteristics on the cloud computing services. High availability ensures that the cloud applications are sensible to failure. Elasticity is a key feature of cloud computing where availability of resources are related with the runtime demand. So in this paper we present a comprehensive framework for obtaining elasticity and scheduling technique for highly availability.

References
[1] M. Stonebraker, U. C¸ etintemel, and S.B. Zdonik, “The 8 Requirements of Real-Time Stream Processing,” SIGMOD Record, vol. 34, no. 4, pp. 42-47, 2005.
[2] S. Chandrasekaran, O. Cooper, A. Deshpande, M.J. Franklin, J.M. Hellerstein, W. Hong, S. Krishnamurthy, S. Madden, V. Raman, F. Reiss, and M.A. Shah, “Telegraphcq: Continuous Dataflow Processing for an Uncertain World,” Proc. First Biennial Conf.Innovative Data Systems Research (CIDR), 2003.
[3] D.J. Abadi, Y. Ahmad, M. Balazinska, U. C¸ etintemel, M. Cherniack, J.-H. Hwang, W. Lindner, A. Maskey, A. Rasin, E. Ryvkina, N. Tatbul, Y. Xing, and S.B. Zdonik, “The Design of the Borealis Stream Processing Engine,” Proc. Second Biennial Conf. Innovative Data Systems Research (CIDR), pp. 277-289, 2005.
[4] M.T. O ¨ zsu and P. Valduriez, Principles of Distributed Database Systems, third ed. Springer, 2011
[5] V. Gulisano, R. Jime´nez-Peris, M. Patin˜ o-Mart?´nez, and P. Valduriez, “Streamcloud: A Large Scale Data Streaming System,” Proc. Int’l Conf. Distributed Computing Systems (ICDCS ’10), pp. 126-137, 2010.
[6] StreamCloud: An Elastic and Scalable Data Streaming System ,Vincenzo Gulisano,Ricardo Jime´nez-Peris,Marta Patin˜ o-Mart?´nez,Claudio Soriente,Patrick Valduriez VOL. 23, NO. 12, DECEMBER 2012.
[7] N. Tatbul, U. C¸ etintemel, and S.B. Zdonik, “Staying Fit: Efficient Load Shedding Techniques for Distributed Stream Processing,” Proc. Int’l Conf. Very Large Data Bases (VLDB), pp. 159-170, 2007.
[8] D.J. Abadi, D. Carney, U. C¸ etintemel, M. Cherniack, C. Convey, S. Lee, M. Stonebraker, N. Tatbul, and S.B. Zdonik, “Aurora: A New Model and Architecture for Data Stream Management,” VLDB J., vol. 12, no. 2, pp. 120-139, 2003.
[9] Y. Xing, S.B. Zdonik, and J.-H. Hwang, “Dynamic Load Distribution in the Borealis Stream Processor,” Proc. Int’l Conf. Data Eng. (ICDE), pp. 791-802, 2005.

Keywords
Scalability, Elasticity, High availability, Load balancing, Reliability.