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Seminar Introduction to Traffic Engineering. October 2009 Ernst Nordström [email protected] Traffic levels. Traffic characterization. Source traffic parameters Peak packet rate Mean packet rate Maximum burst size Minimum packet rate Call class characterization

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traffic characterization
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
definition of traffic processes
Definition of traffic processes
  • Point process:
  • Count process:
  • Inter-arrival time process:
inter arrival time distributions
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
traffic models
Traffic models
  • Renewal traffic models
  • Markov-based traffic models
  • Self-similar traffic models
  • Autoregressive models
markov based traffic models
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
markov modulated fluid process
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
quality of service qos
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)
quality of experience qoe
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
grade of service gos
Grade of Service (GoS)
  • Call blocking probability
  • Call set up delay
traffic engineering te
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
te functions
TE functions
  • Traffic management
  • Capacity management
  • Traffic measurement
  • Traffic modeling
  • Network modeling
  • Performance analysis
traffic and capacity management
Traffic and capacity management
  • Traffic and capacity management relies on a relationship between three models:
    • traffic model
    • network model
    • performance model
importance of te
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
te planning
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
te planning problems
TE planning problems
  • Call admission control and QoS evaluation
  • QoS routing
  • Data center design
  • Network design and GoS evaluation
packet traffic models
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
qos evaluation
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
call admission control
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
unicast routing
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
multicast routing
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
data center design
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
core and metro physical network design
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
core and metro virtual network design
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
correlation in arrival process
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
network operation modes
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
handling of congestion
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
burst versus packet level simulation
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
conclusions for iptv networks
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
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