A MapReduce Model to Process Massive Switching Center Data Set

Wen Chuan Yang; Rui Li; Zhi Dong Shang

doi:10.4028/www.scientific.net/AMM.548-549.1557

Paper Titles

Research and Application of Information Management for NC Equipment
p.1536

A Level-Dependence Model and it’s Application in SFMEA
p.1543

Information Processing in Searching Tweets
p.1549

A Research of HTN-Based Anytime Planning Algorithm on FPS Games
p.1553

A MapReduce Model to Process Massive Switching Center Data Set
p.1557

Pixel Value Graphical Password Scheme: Identifying Design Features and Requirements
p.1561

Study on the Multi-Layer Structure Design of the Power Marketing Demand Side Monitoring Center System
p.1566

Modeling and Simulation of Indoor Millimeter Wave Propagation Characteristics
p.1571

Numerical Simulation of Dynamic Compaction Construction
p.1581

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 548-549A MapReduce Model to Process Massive Switching...

A MapReduce Model to Process Massive Switching Center Data Set

Abstract:

Accompany the widely use of smart phone in China, all inputs and routes packets streams to the Telecommunication Content Distribution Service Switching Centers (TSC). There is a tendency to put more capability into the switch, such as retain or query passing by data. Thus we definitely need to think about what can be kept in working storage and how to analysis it. Obviously, the ordinary database cannot handle the massive dataset and complex ad-hoc query. In this paper, we propose MRTSC, a MapReduce deep service analysis system based on Hive/Hadoop frameworks. A distributed file system HDFS is used in MRTSC for fast data sharing and query. MRTSC also optimizes scheduling for switch analysis jobs and supports fault tolerance for the entire workflow. Our results show that the model achieves a higher efficiency.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 548-549)

Pages:

1557-1560

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.548-549.1557

Citation:

Cite this paper

Online since:

April 2014

Authors:

Wen Chuan Yang*, Rui Li, Zhi Dong Shang

Keywords:

Big Data, Cloud Computing, Hadoop, MapReduce, Workflow

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] P. Barham, B. Dragovic, K. Fraser, S. Hand, T. Harris, A. Ho, R. Neugebauer, I. Pratt, and A. Warfield. Xen and the art of virtualization. In Proceedings of the ACM Symposium on Operating Systems Principles, (2003).

DOI: 10.1145/945445.945462

Google Scholar

[2] A. AuYoung, L. Grit, J. Wiener, and J. Wilkes. Service contracts and aggregate utility functions. In Proceedings of the IEEE International Symposium on High Performance Distributed Computing (HPDC), June (2006).

DOI: 10.1109/hpdc.2006.1652143

Google Scholar

[3] R. Avnur and J. M. Hellerstein. Eddies: Continuously adaptive query processing. In ACM SIGMOD: International Conference on Management of Data, (2007).

DOI: 10.1145/342009.335420

Google Scholar

[4] R. E. Bryant. Data-intensive supercomputing: The case for DISC. Technical Report CMU-CS-07-128, Carnegie Mellon University, (2007).

Google Scholar

[5] K. Cardona, J. Secretan, M. Georgiopoulos, and G. Anagnostopoulos. A grid based system for data mining using MapReduce. Technical Report TR-2007-02, AMALTHEA, (2007).

Google Scholar

[6] B. N. Chun, P. Buonadonna, A. AuYoung, C. Ng, D. C. Parkes, J. Shneidman, A. C. Snoeren, and A. Vahdat. Mirage: A microeconomic resource allocation system for SensorNet testbeds. In Proceedings of the 2nd IEEE Workshop on Embedded Networked Sensors, (2005).

DOI: 10.1109/emnets.2005.1469095

Google Scholar