Establishing Virtual Private Network Bandwidth Requirement at the University of Wisconsin Foundation by Joe Madden In conjunction with ECE 539 Introduction to Artificial Neural Networks and Fuzzy Systems Fall 2010Page 1 Abstract The focus of this study is to determine the optimal allotment of bandwidth for users at the University of Wisconsin Foundation whom connect to the physical network via a Virtual Private Network connection. By the use of artificial neural network pattern classification techniques, the UW Foundation can ensure quality of services amongst all of its connection types, which is critical to business success. The objective is to determine an accurate estimate of usage based on a classification system that examines in-going and out-going packets. Maximum likelihood estimation, k-Nearest Neighbor methods, and multi-layer perceptron modeling have been used to determine the optimal setting.Page 2 Table of Contents Background 3 Objective 3 Methodology 3 Analysis 4 Maximum Likelihood Estimation (Gaussian) 4 K-Nearest Neighbor Classification 6 Back Propagation Multi-Layer Perceptron 10 Discussion 12 References 14Page 3 Background The University of Wisconsin Foundation is the organization responsible for acquiring funding for academics at the University of Wisconsin-Madison. Many employees at the UW-Foundation travel to locations around the world to meet with alumni and corporate sponsors to gain involvement with the university. The Information Technology department at the UW-Foundation has provided its employees the ability to connect to its network while away from the office via a Cisco Virtual Private Network (VPN) client. Each time a person connects, a log captures the employee who makes the connection, the length of connection, and the amount of data that is transferred over the network. Objective Since December 2008, the University of Wisconsin Foundation has been capturing the VPN log information. Roughly 2500 VPN connections have been made, and concepts learned in Introduction to Artificial Neural Networks and Fuzzy Systems will be used to adequately determine the minimum bandwidth needed to operate the VPN client for years to come. Allocating the minimum amount of resources can allow for more attention to other critical areas of the organization, while ensuring Quality of Service is available for the VPN connections. Methodology The study utilizes a data set which includes incoming and outgoing packets, which would comprise the feature space ([2x1]). The class vector would be similar to the ones used in class ([3x1]). The class vector will be either a [1 0 0], [0 1 0], or [0 0 1]. It will be based on a formula which examines strictly the length of the session. The length of session is used as a classifying parameter, because of the following ANOVA table: Response for study: Session Length Predictor Coef Standard Error Coef T Constant 3831.4 309.1 12.39 Incoming Packets 0.97936 0.03787 25.86 Outgoing Packets -0.56696 0.02652 -21.38 As one can see, the session length time is statistically significant in terms of identifying levels of incoming and outgoing packets. Therefore to simplify the class vector, the session length time is the only indicator. Next, a classification set has been determined to equally distribute the class vectors evenly. The first method for classifying the points was sorting the time from shortest to longest and providing one third of each data set into a bin. For the purposes of using a three way cross-Page 4 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2x 10400.511.522.533.544.55x 104Outgoing PacketsIncoming PacketsIncoming vs Outgoing Packets OutgoingIncomingvalidation study, the original data set will be used. The additional data sources were created by changing the sorting scheme to examine the incoming and outgoing packets, respectively. After a training set has been created, I will perform the following tests. Pattern Classification (Maximum Likelihood Estimation) and Clustering (Kmeans and self-organizing map) tests will be performed. Then a testing vector will be examined to determine a best case estimate. The final examination will utilize a backward-propagation multi-layer perceptron model tested at a variety of useful parameters seen throughout the course. Each of the tests will be analyzed, and compared to traditional statistical analysis. The objective of the tests is to beat a conservative estimate made by a 95% confidence interval of the historical data. Analysis Pattern Classification (Maximum Likelihood with Gaussian Likelihood Function) The first section of analysis from the VPN logs was a method for placing an optimal class label on the testing vector. „mldemo_uni.m‟ was the responsible matlab file for the analysis, and it seemed to yield fairly poor results. The confusion matrix and classification rate are as follows: Confusion Matrix 465 86 70 196 245 166 25 155 411 Classification Rate: 61.63% By inspection, it appears the first and third classes had a much easier time being classified than the second. The following is a plot of the results:Page 5 1000 2000 3000 4000 5000 60000100020003000400050006000700080009000Outgoing PacketsIncoming PacketsIncoming vs Outgoing Packets OutgoingIncomingClassified Point Attempt0 2 4 6 8 10 12 14 16 18x 10400.511.522.5x 105Outgoing PacketsIncoming PacketsIncoming vs Outgoing Packets OutgoingIncomingClassified Point Attempt Based on the chart, it is clear that the outgoing and incoming packets roughly follow a linear distribution. A log-normal classification algorithm was used, and is likely the reason for such a poor classification rate. . As one can see, the classification image reflects the poor overall rate. It is interesting to note from this image, that the packets classified near the origin are not easily picked up by the algorithm. The following is a snap-shot of the whole dataset.Page 6 In addition to examining the original data sets, the other two sets provided (sorted by incoming and outgoing packets) were also ran through the same testing. Their classification rates are: Packets Sorted Classification Rate Incoming 45.46% Outgoing 45.19% k-Nearest Neighbor Classifier An interesting pattern classification algorithm is the k-Nearest
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