Download Traffic Modeling (1)

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