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Performance Optimization Problems at Various Layers of Telecommunication Networks

Performance Optimization Problems at Various Layers of Telecommunication Networks. Overview of Research G. R. Dattatreya datta@utdallas.edu (Papers at www.utdallas.edu/~datta/papers). Top level list. Physical layer Noise and data estimation in WIreless LANs MAC layer

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Performance Optimization Problems at Various Layers of Telecommunication Networks

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  1. Performance Optimization Problems at Various Layers of Telecommunication Networks Overview of Research G. R. Dattatreyadatta@utdallas.edu (Papers at www.utdallas.edu/~datta/papers)

  2. Top level list • Physical layer • Noise and data estimation in WIreless LANs • MAC layer • Modeling for better channel utilization • Network layer • Bursty traffic through static and Ad Hoc networks • TCP/IP traffic • Application layer project – Alcatel VoIP system • Industry level projects – QuEST Forum and SBC

  3. Salient features of research approach • Each problem is motivated by and formulated for real world current technology applications • Probability Theory and Stochastic Models for problem formulation and analysis • Analytical solutions, with justified sequence of approximations, if necessary • Demonstration through implementation of solutions, simulation, and analysis of simulation results

  4. Physical layer – Wireless LANs(Larry Singh) • Noise affects BER • So does data statistics • Both are also time varying • Figures for noise and data parameters needed for minimum BER receiver design • They should be estimated on-line – as the receiver operates • Elegant, easy to implement solution • Theretical analysis of performance of solution

  5. Wireless LANs (contd.) • Systems: PAM with AGC and Coherent Detection • Open problem: Estimation of AGC parameter • After problem formulation, no need for EE background • Slides from WCNC presentation follow

  6. MAC layer problems • Overall motivation: • Optimal channel utilization through distributed actions by individual transmitters • Approach: • Identify control parameters for each transmitter • Measure observable channel characteristics • Tune the control parameters • Think globally; act locally

  7. MAC problem: Some details • Start with a transmitter protocol • Identify control parameters: • parameters of random wait, p-sensing, etc. • Approximate random variables to lead to Markov chain: • Even a contant 52 micro second is turned into an Exp. rv. With a mean of 52 micro second !! • Try to develop the composite Markov chain for multiple transmitters.

  8. MAC problem (contd.) • Appears to be a very large Markov chain !! (K. Srinivasan) • Try to simplify • even with crude approximations • Try to express overall performance as a function of transmitter control variables: • Assume same values for a control variable of all transmitters

  9. MAC problem (contd.) • Formidable problem • Some groundwork is complete • Other approaches: • Start with modest systems and simpler protocols (only one control variable) • Try simulating with one transmitter searchnig for optimal solution while others use constant control parameters • Extend to distributed searching by all

  10. Network layer research • Mobile ad hoc network performance (S. Kulkarni) • Bursty traffic engineering (S. Kulkarni, M. Qiu) • modeling, generation, analysis, queue and network performance • Cross layer issues: • Using TCP model in Network traffic model • Combining network traffic control with Datalink control -- Joint with Dr. Saquib, EE

  11. Routing in ad hoc networks (S. Kulkarni) Need an algorithm that • Has low overheads • Lowers end-to-end packet delay • “Adapts” to changes in • traffic pattern • topology • Uses multiple routes, if available = load balancing

  12. Routing Strategy • Distributed approach • No global knowledge of topology or traffic • Each node “learns” independently • Local routing decisions

  13. Routing Strategy (contd.) • Probabilistic routing • Nondeterministic path from source (Si) to destination (Sj) • Load balancing using multiple routes • Avoid congestive hot spots • Packets not forwarded to already visited neighbors • Unacknowledged packet delivery

  14. The Algorithm: Statistically Multiplexed Adaptive Routing Technique (SMART)

  15. SMART: Packet Loss Comparison(Static Topology) Poisson Traffic Multiplexed Pareto Traffic

  16. Experiments With Mobility(Dynamic Topology, Self-Similar Traffic) • Random waypoint mobility model • 30 nodes in 1.5km x 2.5km field • Transmitter range 600m • Random destination, nodes move in straight line • Velocity uniformly distributed between 1m/s and some maximum • Nodes do not pause during simulation • Traffic: Self-similar (100-source muxed Pareto) as before

  17. SMART: Packet Delivery Ratio(Dynamic Topology, Multiplexed Pareto Traffic)

  18. Some indicative figures Queue saturation • Poisson: 98-99% load • Bursty traffic: 30-80% load Network saturation • Poisson: load >70-80% • Multiplexed-Pareto: >40-50% Adaptive algorithm reduces losses in static network by • 25% for Poisson traffic • 50% for multiplexed-Pareto traffic Mobility: Delivery figures (datagram) at moderate loads • 80% packet delivery at low speeds • >60% packet delivery at high speeds

  19. Bursty traffic engineering • Origin of burstiness: • Heavy tailed distributions for various traffic aspects • Simplest models: M/G/1 and G/M/1 • M/G/1 leads to unbounded average queue length • Can G/M/1 model burstiness?

  20. G/M/1 model for bursty traffic queue (S. Kulkarni) • Inter-arrival times (IATs) with infinite variance • Very large IATS – is this a problem? • To make up for the modest average, • One large IAT also implies several very small IATs some times – so, bursty

  21. Effect of Traffic Burstiness Effective load Vs Long term load average, iid Pareto IATs

  22. Characterization of bursty traffic • Generation, measurement, estimation are difficult due to random variables with infinite variance • Unbounded variance also leads to “long range dependence (LRD)” • Alternative models that truncate this effect beyond significant levels are developed

  23. Alternative models of bursty traffic • High order autoregressive models • Useful in generation (S. Kulkarni) • Time series (M. Qiu) -- Traffic amounts over successive intervals -- Useful for analysis and parameter estimation • State transition diagrams (M. Qiu) -- Traffic amounts over successive intervals -- Useful for analysis and parameter estimation -- Analysis of TCP/IP traffic (M/G/infinity models) • MMPP (L. Singh) • An intermediate model between exponential and Pareto extremes • Also intermediate between iid and LRD • Parameter estimation in MMPP

  24. State transition diagrams • Poisson arrival of TCPs • Each TCP lasts for Pareto amount of time (due to unbounded variance of file sizes) • Each TCP pumps a Poisson sequence of packets for its duration • Packets from multiple TCPs are multiplexed into the router queue • The heavy tail effect vanishes !!

  25. State transition diagrams (contd.) • Results in a Markovian system • Two discrete variables: (N_tcp, N_pckt) • Unfortunately, non-product form !! • Two dimensional Z-transform • Partial differential equation • Work in progress (M. Qiu)

  26. MMPP model of bursty traffic • Lesson from state transition diagram: • Unbounded variance and Unbounded LRD sum may not be really required • Extended but limited dependability: • MMPP models: Arrival rate may fluctuate with rates controlled by a Markov chain

  27. Characterization of MMPP: Parameter estimation (Larry Singh) • Two stages: • Estimate the mixture of multiple exponential random variables • Formulated as an Optimization problem • Use earlier result to estimate parameters of controlling Markov chain • Sample results follow

  28. Application layer project • Performance of proxy server for VoIP • Alcatel Funded project • Modeling the functioning of the system (from performance point of view) • Analysis; identification of parameters that crucially affect performance • Researching to obtain figures for these parameters • Evaluation of performance figures

  29. Application layer project (contd.) • Important questions that needed answers • Can we shift the required facilities and processing burden from end terminals to a central server • How many end terminals can be supported per server, given a performance requirement • Average delay of call establishment • Chance of not getting service

  30. Recent industry level projects • QuEST Forum and SBC funded • Study of performance and quality of products and systems • Performance: • Figures on reliability, failure times, how well the failuers were dealth with, etc. • Hierarchical model of performance of large systems • Quality: • Hierarchical model from Customer Satisfaction Data

  31. Industry level projects (contd.) • Questions • How well are the performance and quality data correlated • Can we use their trends to identify problem areas for corrective actions

  32. Conclusion • Problems from current technologies • Mathematical modeling • Sequence of approximations • If necessary • as appropriate • Mathematical solutions • Implementation/Simulation • To demonstrate • Especially if approximations have been made • Assessment

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