Network tomography to enhance the performance of software defined network monitoring and management
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The document discusses the application of network tomography to improve software-defined network (SDN) monitoring and management, focusing on the use of the EM algorithm for parameter estimation. It highlights the limitations of traditional measurement methods, such as the impracticality of measuring packet delays at every router and the challenges of memory constraints. Future work aims to enhance data collection and explore more complex network topologies to optimize measurement strategies.
Network tomography to enhance the performance of software defined network monitoring and management
- 1. NETWORK TOMOGRAPHY TO ENHANCE THE PERFORMANCE OF SOFTWARE DEFINED NETWORK MONITORING AND MANAGEMENT K M Sabidur Rahman with Chaitrali Joshi and Tanjila Ahmed (ECS 273) 9/23/20161
- 2. 9/23/20162 Agenda •Network Tomography and SDN •MiniNet Data Collection •EM algorithm •Results from EM implementation in R •Future work
- 3. 9/23/20163 Network Tomography “It is the study of a network’s internal characteristics using information derived from end point data.” - Wikipedia • Statistical theory and algorithms are used to estimate internal characteristics of the network topology • Flexibility of SDN can be used to make better results
- 4. 9/23/20164 Shortcomings of current measurement methods • Impractical to measure packet delays at every router • Proprietary routers are inflexible • Geographic separation of routers • Storing packet information at every node is expensive • Memory constraints • Causes congestion in communication
- 5. 3/14/165 Experimental Setup • Topology setup using Mininet • Single source multiple receiver • Controller for SDN • Python based POX remote controller S2 H2 S1 S3 H3 H4 H1
- 6. 9/23/20166 EM Algorithm •Iterative method for estimation of parameters in a statistical model •It estimates the maximum likelihood of parameters •Where the model depends on unobserved latent variable •E step: Update the conditional expectation(log likelihood) of the parameter by the given observation •M step: Find the parameter that maximizes that quantity
- 7. 9/23/20167 EM Algorithm y = observed end-to-end delay data z = unobserved link delay(latent variable) p=probability of delays on link i Complete data likelihood can be factorized as : where we have to estimate and z from end-to-end delay y
- 8. 9/23/20168 EM Algorithm Complete data likelihood is dependent on likelihood of z as conditional pmf of y given z does not have p parameter. Where ,number of packets that faces delay j on link i
- 9. 9/23/20169 EM Algorithm E Step : M step: This is our updated estimate of Pij.
- 11. 9/23/201611 Results 0 5 10 15 20 25 30 Data Link 2 EM Link 2 Numberofpackets Delay (ms)
- 13. 9/23/201613 Future •Use more data 100,1000,2000 •Use more complex topology •Use SDN control mechanism to find the minimum measurement needed for a network •Explore temporal effects on delays
- 14. 9/23/201614