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  1. SeminarIntroduction to Traffic Engineering October 2009 Ernst Nordström ernstn@bizopt.se

  2. Traffic levels

  3. Traffic characterization • Source traffic parameters • Peak packet rate • Mean packet rate • Maximum burst size • Minimum packet rate • Call class characterization • Orgin-destination node pair • Inter-arrival time distribution • Holding time distribution • Source traffic parameters • Charging rule

  4. Definition of traffic processes • Point process: • Count process: • Inter-arrival time process:

  5. Inter-arrival time distributions • An inter-arrival time distribution P(An≤t) is lightly tailed if its variance is finite • An inter-arrival time distribution P(An≤t) isheavy tailed if its variance is infinite

  6. Traffic models • Renewal traffic models • Markov-based traffic models • Self-similar traffic models • Autoregressive models

  7. Markov-based traffic models • A.A. Markov and A. Kolmogorov pioneered the theory of Markov processes • Markov property: the current state summarizes all relevant information about past states • Non-zero autocorrelations in {An} allow for modeling of traffic burstiness

  8. Markov-modulated Fluid process • Views traffic as a stream of fluid, characterized by a flow rate (e.g. bits per second) • Appropriate when the individual traffic units are numerous relative to the chosen time scale • A continuous-time Markov chain modulates traffic arrival (fluid) rate in states 1, 2, .., m of the state space

  9. FIFO fluid simulation

  10. Quality of Service (QoS) • Objective performance measure • Performance metrics: • Packet loss probability • Mean packet delay • Maximum packet delay (e.g. 95 % quantile) • Packet delay variation • Throughput (bits/second)

  11. Quality of Experience (QoE) • Subjective performance measure • Desribes users satisfaction of all imperfections affecting the service • Performance metrics • Video quality • Channel change time • Blocking probability for VoD requests

  12. Grade of Service (GoS) • Call blocking probability • Call set up delay

  13. Traffic engineering (TE) • TE objective is to deliver desired Quality of Service (QoS) with minimum consumption of network resources • Optimizeeffectivness in terms of proximity to optimality • Optimizesimplicity in terms of time and space complexity

  14. TE functions • Traffic management • Capacity management • Traffic measurement • Traffic modeling • Network modeling • Performance analysis

  15. Traffic and capacity management • Traffic and capacity management relies on a relationship between three models: • traffic model • network model • performance model

  16. Importance of TE • Expansion of network capacity driven by increase in traffic demand • With an effective TE solution fewer call requests need to be rejected leading to an increased revenue • An effective TE solution allows longer time period between capacity upgrades

  17. TE planning • TE Planner software tool for automated selection of TE algorithms • TE complexity is restricted by system reponse time requirements • Find set of TE algorithms with maximal effectivness that provides the desired TE complexity

  18. TE planning problems • Call admission control and QoS evaluation • QoS routing • Data center design • Network design and GoS evaluation

  19. Packet traffic models • Short-range depedent (SRD) models • Superposition of Markov ON/OFF fluid sources • Discrete Autoregressive (DAR) source • Long-range dependent (LRD) models • Superposition of heavy-tailed ON/OFF fluid sources • Fractional Brownian Motion (FBM) source • Hurst parameter H, 0 ≤ H≤ 1, measures self similarity of traffic arrival process

  20. QoS evaluation • By analysis • Determine model for traffic and network resources • Compute analytical QoS solution • By simulation • Use same traffic and network model as in analysis • Simulate random pattern of traffic arrivals, service completions, and network resource occupancy

  21. Call admission control • Accepts/rejects call requests based on expected end-to-end QoE/QoS • Flooding of link states at regular time intervals (1s- 30s) • Generic CAC decision based on uncertain (aged) link state information • Actual CAC decision at each node along the selected routing path

  22. Unicast routing • Native IP network • Best effort – no QoS guarantees • Shortest path routing via IS-IS or OSPF • IP/MPLS network • QoS guarantees by resource reservation • Constraint-based routing with multiple QoS constraints • Hop-by-hop (OSI Layer 3) routing or explicit (OSI Layer 2) routing

  23. Multicast routing • Native IP network • Best effort – no QoS guarantees • PIM SSM • PIM SM • PIM DM • IP/MPLS network • QoS guarantees by resource reservation • P2MP LSP • PIM MPLS

  24. Data center design • Design of central (VHE) and regional (VHO) video server systems • Number of video servers • Video server allocation rule • In-advance transfer of stored video from central VHE to to regional VHOs

  25. Core and metro physical network design • Global physical network (PN) configuration • Assign user community (population) to network nodes • Dimension PN link capacities • Global PN re-conguration • Assign network nodes to new or expanded user communities • Adjust PN link capacities • Problem input parameters include user population, viewing preference vector, VoD content duration statistics, and viewer request rate vector

  26. Core and metro virtual network design • Virtual networks (VNs) • Overlay network on top of PN • Built by TE-LSPs or ATM VPs • Topology of VN can be different than for PN • Many VN links can share a PN link • VN link can consist of multiple successive PN links • Global VN configuration • Global VN re-configuration

  27. Correlation in arrival process • Buffer distribution is a function of the autocorrelation function (ACF) • Impact of correlation in arrival process becomes nil beyond a time scaled known as correlation horizon • Correlation horizon is a function of the maximal buffer size • Only necessary to chose a model of video traffic that captures the correlation structure up to the given correlation horizon

  28. Network operation modes • Network operating under packet scale congestion • Enough resources are allocated to keep the risk of packet-level overload at the output multiplexer within tolerable limits • Short term correlations are most important • Network operating under burst scale congestion • Enough resources are allocated to keep the risk of burst-level overload at the output multiplexer within tolerable limits • Both short- and long-term correlations are important

  29. Handling of congestion • Large buffers are helpful to significantly reduce the loss rate only for SRD traffic • So for video traffic which is LRD, large buffers will not decrease the loss significantly, but may cause exessive delys, which is not tolerated in IPTV networks

  30. Burst- versus packet-level simulation • Burst level simulation can be implemented by Markov fluid traffic models • Fluid simulation on the network is subject to a ripple effect • For very small buffers, packet-level simulation will be more accurate • Packet-level simulation can be implemented by MMPP traffic models

  31. Conclusions for IPTV networks • IPTV networks are most likely to operate under packet scale congestion • Markov traffic models are sufficient for IPTV networks • Traffic smoothing or shaping is recommended and will improve the statistical multiplexing gain   • Simple metods like Chernoff bound could be used by CAC in IPTV networks