Chair of Network Architectures and Services Department of Informatics Technical University of Munich Comparing the Network Modeling Techniques Ivan Kendzor, Max Helm Friday 25 th January, 2019 Chair of Network Architectures and Services Department of Informatics Technical University of Munich
Introduction Purpose of Network Modeling Techniques Advantages of using Network Modeling Techniques • Measuring of network performance in the real systems • Monitoring and obtaining precise metrics of the network behavior • Predicting of impact of changes and architecture decision on flow performance • Avoids disruption in the real system, enabling the assessment of new processes • Modeling promotes and improves learning Network modeling techniques can evaluate such performance metrics: • Throughput • Waiting time • Estimation the delay of networks • Packet processing time and packet loss I.Kendzor — Comparing the Network Modeling Techniques 2
Introduction Classification of Network Modeling Techniques • Analytical models • Hidden Markov Model • Network Calculus I.Kendzor — Comparing the Network Modeling Techniques 3
Introduction Classification of Network Modeling Techniques • Analytical models • Hidden Markov Model • Network Calculus • Machine Learning Models I.Kendzor — Comparing the Network Modeling Techniques 3
Introduction Classification of Network Modeling Techniques • Analytical models • Hidden Markov Model • Network Calculus • Machine Learning Models • Artificial Neural Network Models • Multilayer perceptron neural network model • Radial Basis Function Networks • State-Space Dynamic Neural Network Technique I.Kendzor — Comparing the Network Modeling Techniques 3
Analytical Models Hidden Markov Model • Defines the system of markov process consisting of hidden states. • Core method: Markov Model • Application field: Coverage problem in wireless sensor networks with two goals: • Energy efficiency • Connectivity 1 1 P . Chaturvedi und A. K. Daniel, „Hidden markov model based node status prediction technique for target coverage in wireless sensor networks“, in 2017 International Conference on Intelligent Communication and Computational Techniques (ICCT), Jaipur,2017, S. 223–227. I.Kendzor — Comparing the Network Modeling Techniques 4
Analytical Models Wireless Sensor Networks A network of devices that can communicate the information gathered from a monitored field through wireless links Figure 1: Wireless Sensor Networks 2 2 Y. El Khamlichi, A. Tahiri, A. Abtoy, I. Medina-Bulo, and F . Palomo-Lozano, “A Hybrid Algorithm for Optimal Wireless Sensor Network Deployment with the Minimum Number of Sensor Nodes,” Algorithms, vol. 10, no. 3, p. 80, Jul. 2017. I.Kendzor — Comparing the Network Modeling Techniques 5
Analytical Models Markov Model • States { S 1 , S 2 , S 3 } • State transitions probability p ij = P [ t j ( t + 1) | t i ( t )]0 ≤ i , j ≤ n � � 0.4 0.6 • Matrix representing the probability of states P = 0.7 0.3 • Initial distribution π = � 0.7 � 0.3 • Markov Model enables to calculate the probability of sequence of states Figure 2: Example of states • Probability of sequence: Sunny - Rainy - Rainy - Sunny: 0.3 ∗ 0.6 ∗ 0.3 ∗ 0.7 = 0.0378 I.Kendzor — Comparing the Network Modeling Techniques 6
Analytical Models Markov Model Figure 3: Probability of observation sequence of lenght 3 3 3 P . Chaturvedi und A. K. Daniel, „Hidden markov model based node status prediction technique for target coverage in wireless sensor networks“, in 2017 International Conference on Intelligent Communication and Computational Techniques (ICCT), Jaipur,2017, S. 223–227. I.Kendzor — Comparing the Network Modeling Techniques 7
Analytical Models Hidden Markov Model • Observation probability matrix b j ( k ) = P [ a k ( t ) | t j ( t )]0 ≤ j ≤ n , 0 ≤ k ≤ m � � 0.1 0.5 0.4 • Matrix representing the probability of observations P = 0.6 0.3 0.1 Figure 4: Example of observations • Observation sequence probability P ( x , a ) = π a 0 q a 0 ( a 0 ) p a 0 a 1 q a 1 ( a 1 ) p a 1 a 2 q a 2 ( a 2 ) Figure 5: Probability of states at various places for observation sequence of length 3 I.Kendzor — Comparing the Network Modeling Techniques 8
Analytical Models Network Calculus • A theory of deterministic queuing systems for the internet. • Core method: Cumulative Rate Function • Application field: • Performance analysis of Network Coding 4 • Reduction of data transmission amount in wireless sensor networks 5 4 H. Li, X. Liu, W. He, J. Li, und W. Dou, „End-to-End Delay Analysis in Wireless Network Coding: A Network Calculus-Based Approach“, in 2011 31st International Conference on Distributed Computing Systems, Minneapolis, MN, USA, 2011, S. 47–56. 5 L. Jiang, L. Yu, und Z. Chen, „Network calculus based QoS analysis of network coding in Cluster-tree wireless sensor network“, in 2012 Computing, Communications and Applications Conference, Hong Kong, China, 2012, S. 1–6. I.Kendzor — Comparing the Network Modeling Techniques 9
Analytical Models Network Coding Example Figure 6: Example of Packet Switch Method Figure 7: Example of Network Coding I.Kendzor — Comparing the Network Modeling Techniques 10
Analytical Models Network Calculus Main components: • Arrival curve • Service curve Figure 8: Example of Network Calculus 6 6 H. Li, X. Liu, W. He, J. Li, und W. Dou, „End-to-End Delay Analysis in Wireless Network Coding: A Network Calculus-Based Approach“, in 2011 31st International Conference on Distributed Computing Systems, Minneapolis, MN, USA, 2011, S. 47–56. I.Kendzor — Comparing the Network Modeling Techniques 11
Machine Learning Network Models Machine Learning Modeling Techniques • Core method: machine-learning algorithms • Application field:network optimization problem; estimating quality of transmissions Machine Learning Models are based on two enabling technologies: • Software-defined networking • Network analytics Most applied machine learning algorithms: • Linear regression • Linear discriminant analysis • Naive bayes • k-nearest neighbors I.Kendzor — Comparing the Network Modeling Techniques 12
Machine Learning Network Models Machine Learning Modeling Techniques Figure 9: Example of "what-if" Machine Learning Algorithms 7 7 F . Geyer und G. Carle, „Towards automatic performance optimization of networks using machine learning“, in 2016 17th International Telecommunications Network Strategy and Planning Symposium (Networks), Montreal, QC, Canada, 2016, S. 19–24. I.Kendzor — Comparing the Network Modeling Techniques 13
Artificial Neural Networks Artificial Neural Networks Models • Core method: backpropagation algorithms • Application field: • Electromagnetic simulation • Modeling of power amplifiers and circuits • Modeling input-output relationship Structure: • Main element of artificial neural network is neuron • ANN and is composed of three components • a set of connecting links (synapses) • cumulative function for summing the input signals • activation function to control the amplitude of the output • Neurons consist of the following layers: • one Input layer • one Output layer • one or many Hidden layers I.Kendzor — Comparing the Network Modeling Techniques 14
Artificial Neural Networks Method of backpropagation Figure 10: Overview of Artificial Neural Network Model 8 I.Kendzor — Comparing the Network Modeling Techniques 15
Artificial Neural Networks General Overview of Network Modeling Techniques Figure 11: General Summary of Network Modeling Techniques I.Kendzor — Comparing the Network Modeling Techniques 16
Bibliography F. Geyer und G. Carle, „Towards automatic performance optimization of networks using machine learning“, in 2016 17th Interna- tional Telecommunications Network Strategy and Planning Symposium (Networks), Montreal, QC, Canada, 2016, S. 19–24. P . Chaturvedi und A. K. Daniel, „Hidden markov model based node status prediction technique for target coverage in wireless sensor networks“, in 2017 International Conference on Intelligent Communication and Computational Techniques (ICCT), Jaipur, 2017, S. 223–227. S. Gebert, T. Zinner, S. Lange, C. Schwartz, und P . Tran-Gia, „Performance Modeling of Softwarized Network Functions Using Discrete-Time Analysis“, in 2016 28th International Teletraffic Congress (ITC 28), Würzburg, Germany, 2016, S. 234–242. H. Li, X. Liu, W. He, J. Li, und W. Dou, „End-to-End Delay Analysis in Wireless Network Coding: A Network Calculus-Based Approach“, in 2011 31st International Conference on Distributed Computing Systems, Minneapolis, MN, USA, 2011, S. 47–56. D. E. Rumelhart, G. E. Hinton, und R. J. Williams, „Learning representations by back-propagating errors“, Nature, Bd. 323, S. 533, Okt. 1986. S. Yan, W. Shi, und J. Wen, „Review of neural network technique for modeling PA memory effect“, in 2016 IEEE MTT-S Interna- tional Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization (NEMO), Beijing, China, 2016, S. 1–2. I.Kendzor — Comparing the Network Modeling Techniques 17
Recommend
More recommend
