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Reseach Article

A Combined Scheme for Controlling GSM Network Calls Congestion

by Alarape Moshood Alabi, Akinwale Adio Taofiki, Folorunso Olusegun
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 14 - Number 3
Year of Publication: 2011
Authors: Alarape Moshood Alabi, Akinwale Adio Taofiki, Folorunso Olusegun
10.5120/1848-2333

Alarape Moshood Alabi, Akinwale Adio Taofiki, Folorunso Olusegun . A Combined Scheme for Controlling GSM Network Calls Congestion. International Journal of Computer Applications. 14, 3 ( January 2011), 47-53. DOI=10.5120/1848-2333

@article{ 10.5120/1848-2333,
author = { Alarape Moshood Alabi, Akinwale Adio Taofiki, Folorunso Olusegun },
title = { A Combined Scheme for Controlling GSM Network Calls Congestion },
journal = { International Journal of Computer Applications },
issue_date = { January 2011 },
volume = { 14 },
number = { 3 },
month = { January },
year = { 2011 },
issn = { 0975-8887 },
pages = { 47-53 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume14/number3/1848-2333/ },
doi = { 10.5120/1848-2333 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:02:29.163210+05:30
%A Alarape Moshood Alabi
%A Akinwale Adio Taofiki
%A Folorunso Olusegun
%T A Combined Scheme for Controlling GSM Network Calls Congestion
%J International Journal of Computer Applications
%@ 0975-8887
%V 14
%N 3
%P 47-53
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Network congestion and signal quality degradation are the major problems of the Global System for Mobile communication (GSM) most especially as the number of customers increases. They are issues that constantly and continuously demand for further researches to improve network performance. Congestion in various systems has always been tackled with various attempts, all of which falls in either the congestion avoidance category or congestion management category. Congestion avoidance has however been adjudged the best scheme for controlling network congestion and this is the approach employed in this research work. The conventional GSM network calls congestion control methods such as Token Bank, Automatic Call Gapping among which Call Admission Control (CAC) is the best, was selected for this work. Dynamic load balancing technique was combined with CAC to re-route calls that would have been dropped to another less busy cell within the Base Station Controller (BSC) area. Dijkstra shortest path algorithm was used to find the shortest route to which calls can be transferred among the collocated base station cells. The combined algorithms were implemented on JAVA platform using real life call data record (CDR) collected from Globacom Nigeria Limited. New Call Blocking and Handoff Call Dropping Probabilities (NCBP and HCDP) were used to measure the performance results. The two probabilities were computed for both CAC only and the combined scheme. The results obtained showed that there is significant reduction in the values of both NCBP and HCDP by 71.43% and 100% respectively, of cells considered for the new combined scheme when compared with that of the CAC only. This indicates that the new scheme has further reduced the values of the NCBP and HCDP which enabled the cells to accommodate more calls thereby increasing the efficiency of the network performance.

References
  1. GSMA 2010. The World GSM Coverage map. Available at www.gsmworld.com/roaming/GSM_WorldPoster2010A.pdf. Accessed on 20th August, 2010.
  2. Rappaport, T. S. 1996. Wireless Communications: Principles and Practice. Upper Saddle River, N. J. London. Prentice Hall PTR, Prentice Hall International.
  3. Narayannan, L. 2002. Channel Assignment and Graph Multicoloring. In Handbook of Wireless Communications and Mobile Computing. Pp 71 – 94.
  4. Wireless Networks Spring. 2005. Available at www.ccs.neu.edu/home/rraj/courses/G250/.../CellularNetworks.ppt. Accessed on 19th October, 2008.
  5. Ayeni, J. O. A. 1992. Fundamentals of Computing. University of Lagos Press, Akoka, Lagos.
  6. Tanenbaum, A. S. 2001. Modern Operating Systems, 2nd Edition, Prentice Hall Inc., Upper Saddle River, New Jersey.
  7. Zuikevicuite, V. and Pedone, F. 2008. Conflict-Aware Load-Balancing Techniques for Database Replication. SAC’08. Fortaleza, Ceara, Brazil.
  8. Mcmillen, C., Stubbs, K., Rybski, P. E., Stoeter, S. A., Gini, M., and Papanikolopoulos, N. 2002. Resource Schedulling and Load Balancing in Distributed Robotic Control Systems. Available at www.colinm.org/papers/McMillen-2002-IAS-final.pdf. Accessed on 10th September, 2008.
  9. Aversa, L. and Bestavros, A. 1999. Load Balancing a Cluster of Web Servers. Available at www.cs.bu.edu/techreports/1999-001-dpr-cluster-load-balancing.pdf
  10. Nehra, J., Patel, R. B., and Bhat, V. K. 2007. A Framework for Distributed Dynamic Load Balancing in Heterogeneous Cluster. Journal of Computer Science, Vol. 3, No. 1, Pp. 14 – 24.
  11. Johansson, H. and Steensland, J. 2006. A Performance Characteristics of Load Balancing Algorithms for Parallel SAMR Applications. Available at: www.it.uu.se/research/publications/lic/2006-010/paperC.pdf. Accessed on 20th August, 2008.
  12. Zhang, H. 2008. On Load Balancing Model for Cluster Computers. International Journal of Computer Science and Network Security. Vol. 8, No. 10, pp. 263 – 269.
  13. Fang, X. and Ghosal, D. 2003. Analysing Packet Delay Across A GSM/GPRS Network. Available at www.cs.ucdavis.edu/research/tech-report/2003/CSE-2003-3.pdf Accessed on 10th September, 2008.
  14. Du, L., Bigham, J. and Cuthbert, L. 2004. A Bubble Oscillation Algorithm for Distributed Geographic Load Balancing in Mobile Networks. IEEE INFOCOM 2004.
  15. Megha, G. and Sachan, A. 2007. Distributed Dynamic Channel Allocation Algorithm for Cellular Mobile Network. Journal of Theoretical and Applied Information Technology. Vol. 16, No 2, pp. 58 – 63.
  16. Smys, S. and Bala, G. J. 2009. K-connection Maintenance algorithm for Balanced Routing in Mobile Ad Hoc Networks. International Journal of Computer Networks and Communications (IJCNC). Vol. 1, No 3, pp. 105 – 111.
  17. Jain, P. and Gupta, D. 2009. An Algorithm for Dynamic Load Balancing in Distributed Systems with Multiple Supporting Nodes by Exploiting the Interrupt Service. International Journal of Recent Trends in Engineering. Vol. 1, No. 1, pp. 232 – 236. Academy Publisher.
  18. Rachida, A., Mustapha L., AbdelAziz, M. Z., Malika, B., and Mehammed, D. 2009. Load Balancing: An Approach Based on Clustering in Ad Hoc Networks. Journal of Computing and Information Technology – CIT. Vol. 17, No 2, pp. 177 – 184.
  19. Chhabra, A., Singh, G., Waraich, S.S., Sidhu, B., and Kumar, G. 2006. Qualitative Parametric Comparison of Load Balancing Algorithms in Parallel and Distributed Computing Environment. Proceedings of World Academy of Science, Engineering and Technology (PWASET). Vol. 16, Pp. 39-42. Available at www.waset.org/journals/waset/v16/v16-8.pdf.
  20. Mileff, P. and Nehez, K. 2005. Fuzzy Based Load Balancing for J2EE Applications. Production Systems and Information Engineering. Vol 3. Pp. Available at http://alpha.iit.uni-miskolc.hu/publications/pubs/2005c.pdf
  21. Bustos-Jimenez, J., Caromel, D., and Piquer, J. M. 2006. Load Balancing: Towards the Infinite Network. A CoreGrid Technical Report Number TR-0049. Available at http://www.coregrid.net/mambo/images/stories/TechnicalReports/tr-0049.pdf. Accessed on 10th May, 2008.
  22. Karger, D. R. and Ruhl, M. 2004. Simple Efficient Load Balancing algorithms for Peer-to-Peer Systems. Available at www.iptps04.cs.ucsd.edu/papers/karger- load-balance.pdf. Accessed on 25th August, 2008.
  23. Ali, A. and Belal, M. A. 2007. Multiple Ant Colonies Optimization for Load Balancing in Distributed Systems. ICTA ’07 Hammamet, Tunisia.
  24. Visalakshi, P. and Sivanandam, S. N. 2009. Dynamic Task Scheduling with Load Balancing using Hybrid Particle Swarm Optimization. International Journal of Open Problems in Computing and Mathematics. Vol. 2, No 3, pp. 475 – 488.
  25. Karagiannis, G. 2002. Scalability and Congestion Control in Broadband Intelligent and Mobile Networks. A Ph. D. Dissertation submitted to the Centre of Telematics and Information Technology, University of Twente.
  26. Peha, J. M. 1997. Scheduling and Admission Control for Integrated Services Networks: The Priority Token Bank. Available at http://repository.cmu.edu/cgi/viewcontent.cgi?article=1046$context=epp.
  27. Hou, J., Yang, J., and Papavassiliou, S. 2002. Integration of Pricing with Call Admission Control to Meet QoS Refinement in Cellular Networks. IEEE Transactions on Parallel and Distributed Systems, Vol. 13, No 9, pp. 898 – 910.
  28. Fang, Y. and Zhang, Y. 2002. Call Admission Control Schemes and Performance Analysis in Wireless Mobile Networks. IEEE Transactions on Vehicular Technology, Vol. 51, No 2, Pp. 371 – 382.
  29. Nasser, N. and Hussanein, H. 2004. Combined Admission Control Algorithm and Bandwidth Adaptation Algorithm in Multimedia Cellular Networks for QoS Provisioning. IEEE. Pp 1183 – 1186.
  30. Oyebisi,, T. O. and Ojesanmi, O. A. 2005. Development of Congestion Control Scheme for Wireless Mobile Network. Journal of Theoretical and Applied Information Technology. Pp. 966 – 972.
  31. Sanabani, M., Shamala, S., Othman, M., and Desa, J. 2006. Adaptive Call Admission Control for Prioritized Adaptive Services in Wireless/Mobile Multimedia Cellular Networks. IJCSNS, International Journal of Computer Science and Network Security. Vol. 6, No. 3B, pp. 114 – 124.
  32. Wu, B. Y. and Chao, K-M 2004. Spanning Trees and Optimization Problems. CRC Press Company. Chapman & Hall/CRC. New York.
Index Terms

Computer Science
Information Sciences

Keywords

Congestion control Call Admission Control Load Balancing NCBP HCDP