Optimasi Jaringan Serat Optik menggunakan Metode Algoritma Genetika (Studi Kasus Unisma)

Diki Okiandri, Erni Yudaningtyas

Abstract


Increased use of computers in education campus resulted in dense data traffic on communications networks. At the Islamic University of Malang (Unisma) there are more than 500 computers connected to the Internet using a wired media and hotspot access. Unisma existing network infrastructure in current use the Multimode Fiber Optic Backbone cable with static routing and Mesh topology. These lots number of users on the network resulting in dense data traffic that lead to long delays or long queues. Genetic algorithm is a search algorithm that is based on the natural genetic mechanism which also being used in optimizing network performance. This study compared the performance of existing network and a simulation of optimization using Genetic Algorithms. Measurement and retrieval of data consist of transfer time, also we built software engineering using visual studio program as a comparison model.The result of this study shows that optimization using genetic algorithm is able to find the fastest path and increase the speed of transmission of data packets by reducing transfer time by 53.5% and increase the datarate of 54.75% compared to the queuing method used on the existing network.

References


Anderson, J. Q., Boyles, J. L. & Rainie, L. 2012. The future impact of the Internet on higher education: Experts expect more-efficient collaborative environments and new grading schemes; they worry about massive online courses, the shift away from on-campus life, cited on October 2015; http://www.pewinternet.org/topics/Future-of-the-internet.aspx, and http://www.imaginingtheinternet.org.

Lin, X-H et al. 2002. A Genetic Algorithm Based Approach To Route Selection And Capacity Flow Assignment. Computer Communications 26 (2003) 961–974 Elsevier Inc.

Kumar, R. & Kumar, M. 2012. Reliable and Efficient Routing Using Adaptive Genetic Algorithm in Packet Switched Networks, International Journal of Computer Science Issues, Vol. 9, Issue 1, No 3, p168-73, ISSN (Online): 1694-0814 www.IJCSI.org

Kumar, D. R. & Kumar, M. 2010. Exploring Genetic Algorithm for Shortest Path Optimization in Data Networks. Global Journal of Computer Science and Technology, Vol. 10 Issue 11 (Ver. 1.0), p 8-12

Senior, J.M., 2008. Optical Fiber Communications,Principles Dan Practice, third edition. Pearson Education Limited 2009, ISBN: 978-0-13-032681-2

Khandelwal, G., Prasanna, G. & Hota, C. 2011. Probabilistic Routing Using Queuing Theory For Manets. International Journal of Wireless & Mobile Networks (IJWMN), 3, 144-157

Duck, M. & Read, R. 2003. Data Communications and Computer Networks for Computer Scientists and Engineers. Second edition, Pearson Education Limited 1996, 2003

Bonaventure, O. 2011. Computer Networking:Principles,Protocols and Practice. Saylor Foundation, URL: http://www.saylor.org/courses/cs402/

Clark, M. P. 2003. Data Networks, IP and the Internet Protocols, Design and Operation. John Wiley & Sons, Ltd ISBN: 0-470-84856-1

Bisht, N. & Singh, S. 2015. Analytical Study Of Different Network Topologies. International Research Journal of Engineering and Technology (IRJET), e-ISSN: 2395-0056 Volume: 02 Issue: 01, Mar-2015, p88-90

Abuiziah, I. & Shakarneh, N. 2013. A Review of Genetic Algorithm Optimization: Operations and Applications to Water Pipeline Systems. International Journal of Mathematical, Computational, Physical, Electrical and Computer Engineering Vol:7, No:12, 2013.p1782-88




DOI: http://dx.doi.org/10.33021/jmem.v1i02.99

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