Affiliation:
1. Beijing University of Posts and Telecommunications
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.
Publisher
Trans Tech Publications, Ltd.
Reference6 articles.
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).
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).
3. R. Avnur and J. M. Hellerstein. Eddies: Continuously adaptive query processing. In ACM SIGMOD: International Conference on Management of Data, (2007).
4. R. E. Bryant. Data-intensive supercomputing: The case for DISC. Technical Report CMU-CS-07-128, Carnegie Mellon University, (2007).
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).