JOURNAL BROWSE
Search
Advanced SearchSearch Tips
An Efficient Algorithm for Big Data Prediction of Pipelining, Concurrency (PCP) and Parallelism based on TSK Fuzzy Model
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
An Efficient Algorithm for Big Data Prediction of Pipelining, Concurrency (PCP) and Parallelism based on TSK Fuzzy Model
Kim, Jang-Young;
  PDF(new window)
 Abstract
The time to address the exabytes of data has come as the information age accelerates. Big data transfer technology is essential for processing large amounts of data. This paper posits to transfer big data in the optimal conditions by the proposed algorithm for predicting the optimal combination of Pipelining, Concurrency, and Parallelism (PCP), which are major functions of GridFTP. In addition, the author introduced a simple design process of Takagi-Sugeno-Kang (TSK) fuzzy model and designed a model for predicting transfer throughput with optimal combination of Pipelining, Concurrency and Parallelism. Hence, the author evaluated the model of the proposed algorithm and the TSK model to prove the superiority.
 Keywords
Pipelining;Concurrency;Parallelism;Big data;TSK fuzzy model;
 Language
English
 Cited by
 References
1.
GridFTP, Globus Online “http://www.globus.org”

2.
T. Takagi and M. Sugeno, “Fuzzy identification of systems and its applications to modeling and control,” IEEE Trans. Syst., Man, Cybern., vol. 15. pp. 116-132, Jan. 1985 crossref(new window)

3.
M. Sugeno and T. Yasukawa, “A fuzzy-logic-based approach to qualitative modeling,” IEEE Trans, Fuzzy Syst., vol. 1, pp. 7-31, 1993. crossref(new window)

4.
E. Yildirim, J. Kim, and T. Kosar, “Optimizing the sample size for a cloud-hosted data scheduling service,” Proc. 2nd International Workshop on Cloud Computing and Scientific Applications (CCSA in conjunction with CCGRID12), 2012.

5.
J. Kim, E. Yildirim, and T. Kosar, “A highly-accurate and low-overhead prediction model for transfer throughput optimization,” Proc. of DISCS Workshop, November 2012.

6.
B. Allen, J. Bresnahan, L. Childers, I. Foster, G. Kandaswamy, R. Kettimuthu, J. Kordas, M. Link, S. Martin, K. Pickett, and S. Tuecke, “Software as a service for data scientists,” Communications of the ACM, vol.55:2, pp.81–88, 2012. crossref(new window)

7.
E. Yildirim, J. Kim, and T. Kosar, "Modeling Throughput Sampling Size for a Cloud-hosted Data Scheduling and Optimization Service," In Future Generation Computer Systems (FGCS), Vol. 29, No. 7 (2013) pp 1795-1807. crossref(new window)

8.
E. Yildirim, J. Kim, and T. Kosar (Best Paper Award), "How GridFTP Pipelining, Parallelism and Concurrency Work: A Guide for optimizing large dataset transfers," In Proceedings of IEEE/ACM Supercomputing'12 Workshop on Network-Aware Data Management (NDM 2012), Salt Lake City, UT, November 2012.

9.
E. Yildirim, M. Balman, and T. Kosar, “Data-intensive Distributed Computing: Challenges and Solutions for Large-scale Information Management, ch. Data-aware Distributed Computing, IGI-Global, 2012.

10.
E. Yildirim, D. yin, and T. Kosar, “Prediction of optimal parallelism level in wide area data transfers,” IEEE Transactions on Parallel and Distributed Systems, vol. 22, no. 12, pp. 2033-2045, 2011. crossref(new window)