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Traffic levels
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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
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Definition of traffic processes
• Point process:
• Count process:
• Inter-arrival time process:
1nTnTnA
1nnA
t} nT :{nmax N(t)
0t)t(N
0nnT
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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) is heavy tailed if its variance is infinite
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Traffic models
• Renewal traffic models
• Markov-based traffic models
• Self-similar traffic models
• Autoregressive models
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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
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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
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FIFO fluid simulation
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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)
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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
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Grade of Service (GoS)
• Call blocking probability
• Call set up delay
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Traffic engineering (TE)
• TE objective is to deliver desired Quality of Service (QoS) with minimum consumption of network resources
• Optimize effectivness in terms of proximity to optimality
• Optimize simplicity in terms of time and space complexity
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TE functions
• Traffic management
• Capacity management
• Traffic measurement
• Traffic modeling
• Network modeling
• Performance analysis
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Traffic and capacity management
• Traffic and capacity management relies on a relationship between three models: – traffic model– network model– performance model
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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
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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
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TE planning problems
1. Call admission control and QoS evaluation
2. QoS routing
3. Data center design
4. Network design and GoS evaluation
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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Conclusions for IPTV networks
1. IPTV networks are most likely to operate under packet scale congestion
2. Markov traffic models are sufficient for IPTV networks
3. Traffic smoothing or shaping is recommended and will improve the statistical multiplexing gain
4. Simple metods like Chernoff bound could be used by CAC in IPTV networks
