Browse > Article

Effective Index and Backup Techniques for HLR System in Mobile Networks  

김장환 (대덕대 IT계열)
이충세 (충북대학교 컴퓨터과학과)
Publication Information
Journal of KIISE:Computing Practices and Letters / v.9, no.1, 2003 , pp. 33-46 More about this Journal
Abstract
A Home Location Register(HLR) database system manages each subscriber's location information, which continuously changes in a cellular network. For this purpose, the HLR database system provides table management, index management, and backup management facilities. In this thesis, we propose using a two-level index method for the mobile directory number(MDN) as a suitable method and a chained bucket hashing method for the electronic serial number(ESN). Both the MDN and the ESN are used as keys in the HLR database system. We also propose an efficient backup method that takes into account the characteristics of HLR database transactions. The retrieval speed and the memory usage of the two-level index method are better than those of the R-tree index method. The insertion and deletion overhead of the chained bucket hashing method is less than that of the modified linear hashing method. In the proposed backup method, we use two kinds of dirty flags in order to solve the performance degradation problem caused by frequent registration-location operations. For a million subscribers, proposed techniques support reduction of memory size(more than 62%), directory operations (2500,000 times), and backup operations(more than 80%) compared with current techniques.
Keywords
two-level index method; chained bucket hashing method; dirty flag;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Peter, M. G. et al, 'PRISMA/DB: A Parallel, Main Memory Relational DBMS,' IEEE Trans. on Knowledge and Data Engineering, Vol. 4, No. 6, Dec. 1992   DOI   ScienceOn
2 Bayer, R. and McCreight, C., 'Organization and Maintenance of Large Ordered Indexes,' Acta Informatica, Vol. 1, No. 3, pp.290-306, 1972   DOI
3 DeWit, D.J. et al, 'Implementation Techniques for Main Memory Database Systems,' Proc. Intl. Conf. on Management of Data, ACM SIGMOD, Boston, USA, pp. 1-8, June, 1984   DOI
4 Garcia-Molina, H., 'Main Memory Database System: An Overview,' IEEE Trans. on Knowledge and Data Engineering, Vol. 4, No. 6, pp. 509-516, 1992   DOI   ScienceOn
5 Jagadish, H. V. et al, 'Dali: A High Performance Main Memory Storare Manager,' Proc. 20th Intl. Conf. on Very Large Data Bases, Santiago, Chille, pp. 48-59, Sept., 1994
6 TIA/EIA, IS-41(A), Initial Version, Jan. 1991
7 Knuth, D., The Art of Programming, Vol. 3, Sorting and Searching Addison-Wesley, 1973
8 한국통신기술협회, MSC/VLR-HLR/AC간 MAP 신호방식 표준, Jul. 1997
9 Son. S. H., Advances In Real-Time Systems, Prentice Hall, 1995
10 Lehman, T.J and Carey, M.J., 'A Study of Index Structures for Main Memory Database Management Systems,' Proc. 12th Intl. Conf. on Very Large Data, Kuoto, Japan, pp. 294-303, Aug. 1986
11 Lehman, T.J. and Carey, M.J., 'A High Performance Memory-Resident Database System,' Proc. 12th Intl. Conf. on Management of Data, San Francisco, USA, pp. 104-117, 1987   DOI
12 Bayer, R., 'Symmetric Binary B-Trees: Data Structure and Maintenance Algorithms,' Acta Informatica, Vol. 1, No. 3, pp. 290-306, 1972   DOI
13 Comer, D., 'The Ubiquitous B-Tree,' ACMI Computing Surveys, Vol. 1, No. 2, pp. 121-137, 1979   DOI   ScienceOn
14 Litwin, W., 'Linear Hashing: A New Tool For File and Table Addressing,' Proc. 6th Intl. Conf. on Very Large Data, Montreal, Canada, pp. 212-223, Oct. 1980
15 Jagadish, H. V, Silberchatz, A. and Sidarshan, S., 'Recovering from Main-Memory Lapses,' Proc. 19th Intl. Conf on very Large Data Bases, Dubiln, Ireland, pp. 391-404, Aug., 1993