Bandwidth Allocation Mechanism Based on Users' Web Usage Patterns for Campus Networks.

Rolysent K Paredes; Ariel M Sison; Ruji P Medina

No Detail Found!

Bandwidth Allocation Mechanism Based on Users' Web Usage Patterns for Campus Networks.

Author(s)

Rolysent K Paredes Graduate Programs, Technological Institute of the Philippines, Quezon City, Philippines

Ariel M Sison School of Computer Studies, Emilio Aguinaldo College, Manila, Philippines

Ruji P Medina Graduate Programs, Technological Institute of the Philippines, Quezon City, Philippines

Abstract

Managing the bandwidth in campus networks becomes a challenge in recent years. The limited bandwidth resource and continuous growth of users make the IT managers think on the strategies concerning bandwidth allocation. This paper introduces a mechanism for allocating bandwidth based on the users' web usage patterns. The main purpose is to set a higher bandwidth to the users who are inclined to browsing educational websites compared to those who are not. In attaining this proposed technique, some stages need to be done. These are the preprocessing of the weblogs, class labeling of the dataset, computation of the feature subspaces, training for the development of the ANN for LDA/GSVD algorithm, visualization, and bandwidth allocation. The proposed method was applied to real weblogs from university's proxy servers. The results indicate that the proposed method is useful in classifying those users who used the internet in an educational way and those who are not. Thus, the developed ANN for LDA/GSVD algorithm outperformed the existing algorithm up to 50% which indicates that this approach is efficient. Further, based on the results, few users browsed educational contents. Through this mechanism, users will be encouraged to use the internet for educational purposes. Moreover, IT managers can make better plans to optimize the distribution of bandwidth.

Publication Details

Page(s) 380-396

DOI DOI not available

Published Journal: International Journal of Communication Networks and Information Security, Volume: 10, Issue: 2, Year: 2018

Request the full text from PASTIC

References

E. A. Z.Noughabi,B. H.Far,B.Raahemi, 2016.Predicting Students Behavioral Patterns in University Networks for Efficient Bandwidth Allocation: A Hybrid Data Mining Method (Application Paper),” 2016 IEEE 17th International Conference on Information Reuse and Integration (IRI) -

A.Sarigiannidis,P.Nicopolitidis, 2015.Addressing the interdependence in providing fair and efficient bandwidth distribution in hybrid optical-wireless networks,International Journal of Communication Systems 29 1658 -1682

M.Hemmati,B.Mccormick,S.Shirmohammadi, 2016.Fair and Efficient Bandwidth Allocation for Video Flows Using Sigmoidal Programming,2016 IEEE International Symposium on Multimedia (ISM) -

P.Sukumar,L.Robert,S.Yuvaraj, 2016.Review on modern Data Preprocessing techniques in Web usage mining (WUM,” 2016 International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS) -

S. G.Langhnoja,M. P.Barot,D. B.Mehta, 2013.Web usage mining using association rule mining on clustered data for pattern discovery,International Journal of Data Mining Techniques and Applications 2 -

N. B. M.Zainee,K.Chellappan, 2016.A preliminary dengue fever prediction model based on vital signs and blood profile,2016 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES) -

S. S.Nikam, 2015.A comparative study of classification techniques in data mining algorithms,”,Oriental Journal of Computer Science and Technology 8 13 -19

P. P.Markopoulos, 2017.Linear Discriminant Analysis with few training data,” 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) -

S.Yu,Z.Cao,X.Jiang, 2017.Robust linear discriminant analysis with a Laplacian assumption on projection distribution,2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) -

C. L. Liu , W. H.,Hsaio , C. H.,Lee , and F. S.,Gou, 2014.,” IEEE Transactions on Cybernetics 44 989 -1000

W.Deng,J.Hu,J.Guo,ExtendedSRC, 2012.Undersampled Face Recognition via Intraclass Variant Dictionary,”,IEEE Transactions on Pattern Analysis and Machine Intelligence 34 1864 -1870

J.Shao,Y.Wang,X.Deng,S.Wang, 2011.Sparse linear discriminant analysis by thresholding for high dimensional data,”,The Annals of Statistics 39 1241 -1265

R.Mclean,A. J.Walker, 2017.An artificial neural network driven decision-making system for manufacturing disturbance mitigation in reconfigurable systems,” 2017 13th IEEE International Conference on Control & Automation (ICCA) -

S. M.Sharif,F. M.Kusin,Z. H.Asha'Ari,A. Z.Aris, 2015.Characterization of Water Quality Conditions in the Klang River Basin, Malaysia Using Self Organizing Map and Kmeans Algorithm,” Procedia Environmental Sciences, 30 73 -78

P.Howland,H.Park, 2004.Generalizing discriminant analysis using the generalized singular value decomposition,” IEEE Transactions on Pattern Analysis and Machine Intelligence 26 995 -1006

X.Jing,Y.Dong,Y.Yao, 2012.Uncorrelated optimal discriminant vectors based on generalized singular value decomposition,” International Conference on Automatic Control and Artificial Intelligence (ACAI -

C. H.Park, 2005.A Relationship between Linear Discriminant Analysis and the Generalized Minimum Squared Error Solution,”,SIAM Journal on Matrix Analysis and Applications 27 474 -492

Z.Chen,T. H.Chan, 2017.A truncated generalized singular value decomposition algorithm for moving force identification with ill-posed problems,” Journal of Sound and Vibration 401 297 -310

W.Wu,M. O.Ahmad, 2009.Orthogonalized linear discriminant analysis based on modified generalized singular value decomposition,2009 IEEE International Symposium on Circuits and Systems -

S.Bahrami, 2017.Three-dimensional inverse scattering approach using analytical singular value decomposition method,” 2017 18th International Radar Symposium (IRS) -

H.Zhao,P. C.Yuen,PartB, 2008.Incremental Linear Discriminant Analysis for Face Recognition,”,IEEE Transactions on Systems, Man, and Cybernetics 38 210 -221

Y.Dash,S. K.Dubey, 2012.Quality prediction in object oriented system by using ANN: a brief survey,International Journal of Advanced Research in Computer Science and Software Engineering 2 -

S. S.Ranhotra,A.Kumar,M.Magarini,A.Mishra, 2017.Performance comparison of blind and non-blind channel equalizers using artificial neural networks,” 2017 Ninth International Conference on Ubiquitous and Future Networks (ICUFN) -

B.Fan,S.Leng,K.Yang, 2016.A dynamic bandwidth allocation algorithm in mobile networks with big data of users and networks,” IEEE Network 30 6 -10

J.Wang,M.Qiu,B.Guo, 2015.High reliable real-time bandwidth scheduling for virtual machines with hidden Markov predicting in telehealth platform,” Future Generation Computer Systems, 49 68 -76

L.Chitanana,D. W.Govender, 2015.Bandwidth Management in the Era of Bring Your Own Device,”,The Electronic Journal of Information Systems in Developing Countries 68 1 -14

S.Akpah, 2017."Improving the Performance of a Network by Managing the Bandwidth Utilisation with squidGuard: A Case Study,",Ghana Journal of Technology 1 9 -17

K. S.Reddy,M. K.Reddy,V.Sitaramulu, 2013.An effective data preprocessing method for Web Usage Mining,2013 International Conference on Information Communication and Embedded Systems (ICICES) -

T.Kärkkäinen,M.Pitkänen,J.Ott,Dec, 2013.Enabling ad-hoc-style communication in public WLAN hot-spots,” ACM SIGMOBILE Mobile Computing and Communications Review, 17 4 -

A.Dabrowski,N.Kommenda, 2016.Browser History Stealing with Captive Wi-Fi Portals,” 2016 IEEE Security and Privacy Workshops (SPW, -

F. L.Aryeh,M.Asante,A. E. Y.Danso, 2016.Securing Wireless Network Using pfSense Captive Portal with Radius Authentication-A Case Study at UMaT,”,Ghana Journal of Technology 1 40 -45

ShallaSecure Services,KG, 2018.Shalla's Blacklists,”,Shalla Secure Services 17 -

A. C.Squicciarini,A.Nath, 2014.Situational awareness through reasoning on network incidents,”,Proceedings of the 4th ACM conference on Data and application security and privacy - CODASPY 14 -

X.Gao,X.Wang,X.Li,and D.Tao, 2011.Transfer latent variable model based on divergence analysis,” Pattern Recognition 44 2358 -2366

X.Gao,X.Wang,D.Tao,X.Li,PartB, 2011.Supervised Gaussian Process Latent Variable Model for Dimensionality Reduction,”,IEEE Transactions on Systems, Man, and Cybernetics 41 425 -434

J. J. D. M. S. Junior and A. R.,Backes, 2015.Shape classification using line segment statistics,” Information Sciences 305 349 -356

D.Tao,X.Lin,and J.Yu, 2015.Local structure preserving discriminative projections for RGB-D sensorbased scene classification,”,Information Sciences 320 383 -394

D.Wang,X.Gao,X.Wang, 2016.Semi-Supervised Nonnegative Matrix Factorization via Constraint Propagation,”,IEEE Transactions on Cybernetics 46 233 -244

L.Zhang,L.Wang,W.Lin,PartB, 2012.Generalized Biased Discriminant Analysis for Content-Based Image Retrieval,”,IEEE Transactions on Systems, Man, and Cybernetics 42 282 -290

J.Zhao,L.Shi,and J.Zhu,DData, 2015.Two-Stage Regularized Linear Discriminant Analysis for 2,IEEE Transactions on Neural Networks and Learning Systems 26 1669 -1681

G.Baudat,F.Anouar, 2000.Generalized Discriminant Analysis Using a Kernel Approach,” Neural Computation, 12 2385 -2404

S.Mika,J.Weston,B.Scholkopf,K.Mullers, .Fisher discriminant analysis with kernels,” Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468). -

A.Sharma and K. K. Paliwal, 2015.Linear discriminant analysis for the small sample size problem: an overview,International Journal of Machine Learning and Cybernetics 6 443 -454

S.-K.Oh,S.-H.Yoo, 2013.Design of face recognition algorithm using PCA -LDA combined for hybrid data pre-processing and polynomial-based RBF neural networks : Design and its application,” Expert Systems with Applications, 40 1451 -1466

G. C.Lin,C. M.Wang,W. J.Wang, 2007.An Unsupervised Linear Discriminant Analysis Approach to Multispectral MRI Images Classification,2007 International Conference on Machine Learning and Cybernetics -

C.Ding,T.Li, 2007.Adaptive dimension reduction using discriminant analysis and K-means clustering,” Proceedings of the 24th international conference on Machine learning - ICML 07 -

D.Wang,F.Nie,H.Huang,Clustering, 2014.Unsupervised Feature Selection via Unified Trace Ratio Formulation and,” Machine Learning and Knowledge Discovery in Databases Lecture Notes in Computer Science 306 -321

D.Conka,P.Viszlay,J.Juhar, 2015.Improved class definition in two dimensional linear discriminant analysis of speech,” 2015 25th International Conference Radioelektronika (RADIOELEKTRONIKA) -

S.Saraswathi,L.Sivakumar, 2014.Comparative analysis of kmeans and self organizing map clustering on boiler process data,” 2014 International Conference on Computer Communication and Informatics -

Z.Zhang,C.Xu,M. I. Jordan, 2010.Regularized discriminant analysis, ridge regression,” Journal of Machine Learning Research 2199 -2228

T.Zhang,B.Fang,Y. Y.Tang,Z.Shang,B.Xu,PartB, 2010.Generalized Discriminant Analysis: A Matrix Exponential Approach,”,IEEE Transactions on Systems, Man, and Cybernetics 40 186 -197

H.Yu,J.Yang, 2001.A direct LDA algorithm for highdimensional data - with application to face recognition,” Pattern Recognition, 34 2067 -2070

J.Ye,Q.Li, 2005.“A two-stage linear discriminant analysis via QR-decomposition,” IEEE Transactions on Pattern Analysis and Machine Intelligence 27 929 -941

J. K. P.Seng and K. L.-M. Ang, 2017.Big Feature Data Analytics: Split and Combine Linear Discriminant Analysis (SC-LDA) for Integration Towards Decision Making Analytics,” IEEE Access, 5 14056 -14065

H.Abdi, 2007.Singular value decomposition (SVD) and generalized singular value decomposition,” Encyclopedia of measurement and statistics, 907 -912

D.Senaratne,C.Tellambura, 2010.Generalized Singular Value Decomposition for Coordinated Beamforming in MIMO Systems,2010 IEEE Global Telecommunications Conference GLOBECOM -

K.Jazayeri,M.Jazayeri,S.Uysal, 2016.Comparative Analysis of Levenberg-Marquardt and Bayesian Regularization Backpropagation Algorithms in Photovoltaic Power Estimation Using Artificial Neural Network,” Advances in Data Mining,Applications and Theoretical Aspects Lecture Notes in Computer Science 80 -95

F.Dalipi,S. Y.Yayilgan, 2015.The impact of environmental factors to skiing injuries: Bayesian regularization neural network model for predicting skiing injuries,” 2015 6th International Conference on Computing, Communication and Networking Technologies (ICCCNT) -

P.Singh,V.Pareek,A. K.Ahlawat, 2017.Designing an Energy Efficient Network Using Integration of KSOM, ANN,International Journal of Communication Networks and Information Security (IJCNIS) 9 -

Citations

Downloads

143

Views