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Traffic Modeling What is Traffic Modeling Statistical Representation of Traffic generated: Users Telecommunication Network From Users to Network From Network to Users Within Network itself What do we usually Model Packet Inter-arrival behavior Packet payload behavior + Packet time-correlation behavior © Tallal Elshabrawy Packet Arrival Behavior over Time time 2 Why Do We Need Traffic Modeling? Network Dimensioning How many devices + which devices? © Tallal Elshabrawy 3 Why Do We Need Traffic Modeling? QoS Provisioning What is expected QoS peformance? © Tallal Elshabrawy 4 Why Do We Need Traffic Modeling? Connection Admission Control Which connections should be admitted to network? © Tallal Elshabrawy 5 Why Do We Need Traffic Modeling? Congestion Control How to deal with network overload conditions? © Tallal Elshabrawy 6 Traffic at Different Layers in the Network Characteristics of Traffic at each layer is influenced by Usually Layers of Emphasis for Traffic Modeling Obviously, the layer above it For sure, the protocol behavior of the layer itself And even, the operation of the layer below it Nevertheless, traffic modeling attempts to model the composite behavior of each layer individually © Tallal Elshabrawy 7 Traffic at the PHY Layer 01000110101010101011000100111100101011 Sequence of bits streams Modeling and Simulation at the PHY layer are usually concerned with BER and Channel Quality Usually model equal probability of 0 and 1 is assumed © Tallal Elshabrawy 8 Traffic at the MAC+LLC Layer Starting at the MAC layer, packets appear 3 2 4 1 The MAC & LLC will impact packet sizes through the maximum lengths they impose Inter-arrival times are affected by random access behavior along with flow control behavior The traffic models are still predominantly concerned with point-to-point communication © Tallal Elshabrawy Shared Medium M … 5 9 Traffic at the Network Layer Packet Switching Network Multiple Levels of Data Aggregation Multiple Services supported by IP Routing Access Clients © Tallal Elshabrawy 10 Traffic at the Transport Layer TCP or UDP Sliding Window Flow Control of TCP initiate TCP connection RTT Slow Start of TCP request file RTT file received time © Tallal Elshabrawy time to transmit file time 11 Traffic at the Application Layer peer-peer client/server Application layer protocols have different characteristics Client-server or peer-to-peer architectures © Tallal Elshabrawy 12 The Internet Minute © Tallal Elshabrawy 13 The Internet of Things © Tallal Elshabrawy 14 Mobile Data Tremendous Traffic Growth CAGR: Compound Annual Growth Rate 1 Exa Byte = 1 Billion (109) Giga Byte [Ref] Cisco, “Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2013–2018”, White Paper Feb .2014 © Tallal Elshabrawy 15 3GPP Traffic Models Examples: HTTP © Tallal Elshabrawy 16 3GPP Traffic Models Examples: HTTP © Tallal Elshabrawy 17 3GPP Traffic Models Examples: FTP © Tallal Elshabrawy 18 3GPP Traffic Models Examples: NRT Video NRT = Non Real Time © Tallal Elshabrawy 19 3GPP Traffic Models Examples: Speech ON Talk OFF Silent Average ON Period Exponentially Distributed with mean 3 sec. Average OFF Period Exponentially Distributed with mean 3 sec. Packet Data Rate and Packet Size during ON Period is Constant © Tallal Elshabrawy 20 Parameters for Traffic Modeling Parameters needed to model traffic of any type Packet size Inter-arrival times Auto-correlation behavior Packet size might be easier to model. It is mainly subject to protocol behavior Inter-arrival times are more challenging Effect of aggregation Sources of delay within network Auto-correlations may have significant impact on network performance © Tallal Elshabrawy 21 Classical Traffic Modeling: Poisson Models Used originally to model arrivals of calls in a telephone network The distribution is memoryless. Assumes arrivals are independent Very simple and easy to use Can be seen as a counting process. Inter-arrival times follow an exponential distribution Has a single parameter, arrival rate λ Mean and variance also equal to λ Aggregation of Poisson processes remain Poisson 𝑘 −λ λ 𝑒 Pr 𝑋 = 𝑘 = 𝑘! 𝑓 𝑡 = λ𝑒 −λ𝑡 © Tallal Elshabrawy 22 A New Era of Traffic Modeling In 1989, Leland and Wilson begin taking high resolution traffic traces at Bellcore Ethernet traffic from a large research lab 100 m sec time stamps Mostly IP traffic (a little NFS) Four data sets over three year period Over 100m packets in traces Traces considered representative of normal use © Tallal Elshabrawy 23 Why Not Poisson for Web Traffic? Poisson distribution does not scale the Bursty Traffic properly. In fine scale, Bursty Traffic Appears Bursty, In Coarse scale, Bursty Traffic appears smoothed out and looks like random noise. © Tallal Elshabrawy 24 Enhancing the Poisson Model? Compound Poisson Process The model is extended to deliver batches of traffic at once. The inter-batch arrival times are exponentially distributed, while the batch sizes are geometric. The model has two parameters: The mean inter-batch arrival time 1/λ The batch parameters ρ (between 0 and 1) Thus, mean packet arrival over time period t is tλ/ρ The model is still essentially Poisson, which is memoryless © Tallal Elshabrawy 25 Enhancing the Poisson Model? Markov Modulated Poisson Process 1-r1 r1 λ1 λ2 1-r2 r2 Motivated by the need to generate packet arrivals at different rates A continuous-time Markov chain varies the arrival rate of a Poisson model Each state in the Markov chain has an associated arrival rate To determine these parameters, real traffic traces must be used The model is designed to fit the real trace based on metrics such as: mean packet arrival rate, variance-to-mean ratio of the number of arrivals over a short period, or long-term variance-to-mean ratio of the number of arrivals Remains dependent on Memoryless type of of distribution © Tallal Elshabrawy 26 Self-Similar Traffic Models Scales Bursty traffic well, because it has similar characteristics on any scale. Gives a more accurate pictures due to measured web traffic behavior Long Range Dependence in the network traffic © Tallal Elshabrawy 27 Example of Distributions Supporting LRD The Pareto Distribution 𝑓 𝑡 = 𝛽𝛼 𝛽 𝑡 −𝛽−1 α and β are called shape and location parameters LRD = Long Range Dependence © Tallal Elshabrawy 28 Observations from Practical Measurements Characterizing aggregate network traffic is hard Lots of (diverse) applications Just a snapshot: traffic mix, protocols, applications, network configuration, technology, and users change with time © Tallal Elshabrawy Observations from Practical Measurements Packet traffic is bursty Average utilization may be very low Peak utilization can be very high Depends on what interval you use!! Traffic may be self-similar: bursts exist across a wide range of time scales Defining burstiness (precisely) is difficult © Tallal Elshabrawy Observations from Practical Measurements Traffic is non-uniformly distributed amongst the hosts on the network Example: 10% of the hosts account for 90% of the traffic (or 20-80) Why? Clients versus servers, geographic reasons, popular ftp sites, web sites, etc. © Tallal Elshabrawy Observations from Practical Measurements Well over 90% of the byte and packet traffic on most networks is TCP/IP By far the most prevalent Often as high as 95-99% Most studies focus only on TCP/IP for this reason © Tallal Elshabrawy Observations from Practical Measurements Traffic is bidirectional Data usually flows both ways Not JUST acks in the reverse direction Usually asymmetric bandwidth though Pretty much what you would expect from the TCP/IP traffic for most applications © Tallal Elshabrawy Observations from Practical Measurements Packet size distribution is bimodal Lots of small packets for interactive traffic and acknowledgements Lots of large packets for bulk data file transfer type applications Very few in between sizes © Tallal Elshabrawy