Seminar Introduction to Traffic Engineering

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# Seminar Introduction to Traffic Engineering - PowerPoint PPT Presentation

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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### SeminarIntroduction to Traffic Engineering

October 2009

Ernst Nordström

[email protected]

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
• Point process:
• Count process:
• Inter-arrival time process:
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
• Renewal traffic models
• Markov-based traffic models
• Self-similar traffic models
• Autoregressive 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
• 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)
• 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)
• 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)
• Call blocking probability
• Call set up delay
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
• Traffic management
• Capacity management
• Traffic measurement
• Traffic modeling
• Network modeling
• Performance analysis
Traffic and capacity management
• Traffic and capacity management relies on a relationship between three models:
• traffic model
• network model
• performance model
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 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
• Call admission control and QoS evaluation
• QoS routing
• Data center design
• Network design and GoS evaluation
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
• 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
• 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
• 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
• 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
• 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
• 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
• Problem input parameters include user population, viewing preference vector, VoD content duration statistics, and viewer request rate vector
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
• 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 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
• 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 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
• 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