Traffic Engineering Capacity Planning

Requirements Definition Outline Introduction Throughput Calculation Traffic Engineering Basics Basics – Traffic Characteristics Characteristics and Source models 4. Traditional Traffic Engineering Engineering – Voice Traffic Modelling Modelling 5. Queued Data and Packet-Switched Traffic Modelling 6. Designing for Peaks 7. Delay 8. Availability and Reliability 9. Reaction to Extreme Situations 10. Network Performance Modelling 11. Creating Traffic Matrix 12. Capacity Planning and Network Vision 1. 2. 3.

Traffic Engineering and Capacity Planning

EE5302 Network Design and Management

Dr. W Yao, Brunel University

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1. Introduction

Capacity planning is primarily concerned with understanding & quantifying 

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It lays the foundation of network design!

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Advanced protocols, dynamic traffic patterns and characteristics, and peer-to-peer internetworking has changed capacity planning approach than one based on into more of a of a heuristic guesswork approach than calculation. The traffic matrix is no longer a two-dimensional spreadsheet, but a multidimensional matrix including variables such as protocol types, multiple protocols, multiple traffic-flow patterns, multiple technologies, circuit options, and more. EE5302 Network Design and Management

Capacity planning procedures: 

Application behaviour User behaviour for tackling the worst case scenario Traffic characteristics Network performance characteristics, such as network utilization


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1. Introduction 


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Step 1: 1 : Forming a discussion group, including  User Group  Application Group  Network Manger and designer Step 2: Quantifying user behaviour, including  User population by site, building, floor etc  Major user groups  Applications used by user group  Site location date  Expansion or change of plans



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Traffic Engineering and Capacity Planning 


Effects of Overhead

Theoretical Delay Curves for Packet/Frame Transmissions

Example : now change the frame size to 56 bytes. The overhead is still 13 bytes per frame. So we have

512 kbps × 

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

8 bits

×

1 frame 69 Bytes

≈ 928 FPS

Throughput degrades dramatically!

Overhead 1 = 

1 Byte

13 1037

≈ 1.25%

Overhead 2 =

13 69

≈ 18.84%

The larger frame sizes are more efficient and provide higher line throughput than the smaller ones, but up to a certain point. In packet switching, the larger the packet size, the higher the probability of error, causing data to require retransmission. For noise lines, throughput can be increased by decreasing packet size. The added overhead is offset by reduced retrx’s.



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Burstiness is a measure of how infrequently a source sends traffic. A source that infrequently sends traffic is very bursty.

Burstiness =

Deterministic parameters are based upon a specific traffic contract, with conformance verifiable on a unit-by-unit basis. 

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The agreement as to the traffic throughput that achieves a given performance is unambiguously stated.

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Since the method and interval for computing the average can differ, conformance testing defines the details of the measurement method.


Source activity probability is a measure of how frequently the source sends, defined by the probability that a source is bursting.

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1 urstiness

Utilization is a measure of the fraction of a transmission link’s capacity that is used by a source during a time interval.

Peak Utilization =

Specification of the statistical model is also required.


Peak Rate Average Rate

Source Activity Probabilit y =

The probabilistic (also called stochastic) model is typically measurable only over a very long-term average. 

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General Source Model Parame ters

Source Model Traffic Parameter Characteristics 



3. Traffic Engineering Basics – Traffic Characteristics and Sources Models 


Peak Rate Link Rate

All protocol and switching overheads should be accounted for in the calculation of utilization.



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4. Traditional Traffic Engineering 

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Voice Network

Statistical Behavior of User Traffic User information arrives at the network node based on statistical arrival rates. Therefore, statistical approximations can be used to model these traffic patterns.

Voice Traffic Modeling (Erlang Analysis) 

Erlang is the measure used in analog voice communication systems for estimating user demand. It is defined as

Erlang E = λ ⋅ τ =

k

∑ λ

n

⋅ τ n

n =1

where λ is the call arrival rate in calls/hour, and τ is the average call holding time in hours. Example : if 100 calls of 150 second duration, 200 calls of 100 second duration, and 300 calls of 50 second duration within one hour, the number of erlangs would be 13.89.



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While Erlangs work well predicting voice network and circuitswitched traffic rates, they do not work well with packetswitched networks. 

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In packet-switched networks, some level of queueing is employed so that packets are queued in buffers and transmitted when congestion ceases, rather than being immediately blocked. Packet-switched networks provide a mix of protocol and traffic types, whereas voice and circuit-switched networks provide pointto-point, transparent homogeneous transport of information.

Therefore, packet switching demands a different analysis of traffic handling.

Queueing System Models Notation A/B/s/w/p

Queued Data and Packet-Swi tched Traffic Modeling  

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A : arrival process of new calls/packets (A=M, G or D) B: departure process of served calls/packets (B=M, G or D) M : Markovian; G: General; D : Deterministic. s: number of queue servers (s>0) w : size of waiting room (or number of buffer positions, w>0) p: source population (or number of users, p> 0) 

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5. Queued Data and Packet-Switched Traffic Modeling 


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Erlang-B: M/G/s/s  voice blocked calls cleared and FDM/TDM. Erlang-C: M/G/s/k  voice blocked calls held or operator services (this model is used when k>s). Packet: M/G/1  packet, frame, and cell networks (assume infinite waiting room and population).



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M a r k o vi a n Q u eu e i ng S y st e m s M o d e ls 

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Application of M/D/1 and M/M/1 Queueing System with Cells

M/D/1 model 

Constant length bursts

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Buffer unit is packet, frame, or cell

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Difficult to analyze

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not accurate 

accurate

M/M/1 model 

Random length bursts with a negative exponential distribution (memoryless, Markov)  accurate

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Buffer unit is burst

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Simple to analyze


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not accurate


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Relationship Between Utilization and Queue Size

Utilization (or offered load) is a unitless measure to represent the average fraction of the resource capacity that is in use.

ρ =



Utilization and Capacity Calculations 


λ is the averag e number o f arriving bursts per second; , -1 μ μ is the average service time in second per burst. λ

The probability that there are n bursts in the M/M/1 system

Pr{ n bursts in M/M/1 system } = ρ n ⋅ (1 − ρ ). 2 ρ ρ − ρ = . The average queue size is N − ρ = 1 − ρ 1 − ρ ρ The average queueing delay (waiting time) is w = . μ ⋅ (1 − ρ ) is given by

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The average delay is equal to the sum of the waiting time (in the queue) and the service time (at the server), specifically

d avg = w + τ = w + Dr. W Yao, Brunel University

1 μ

=

1 μ ⋅ (1 − ρ )

=

N . λ


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Markovian Queueing Packet Switching System Example 

Relationship Between Utilization and Response Time

If a packet switch has 5 users, each transmitting 10 messages per second at 1024 bits per message, with the packet switch operating over a 56 kbps trunk, the following applies

λ = 5 ⋅10 = 50 mps; μ = ρ =

λ μ

56000 bps 1024 bpm

= 0.914 ⇐ Utilization; N =

= 54.6875 mps ρ

1 − ρ

= 10.63 messages in system

N − ρ = 9.716 messages in queue w=

N μ

= 0.194 seconds ⇐ average waiting time

d avg = w +

1 μ

= 0.194 + 0.0183 = 0.212 seconds ⇐ average d elay



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Realistic source and switch traffic models are not currently amenable to direct analysis, only approximations can be derived under certain circumstances.

Constantly changing source, switch and network characteristics create a moving target for such traffic engineering models.

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+1

Pr{n > B} = Dr. W Yao, Brunel University

∑

∞

P n = ρ B +1

n = B +1


where CLR stands for Cell Loss Ratio and P represents the average number of cells in contained a PDU burst. 

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The probability of overflowing a buffer of size B packets is

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The overflow probability for a buffer of size B cells (M/M/1/B) is approximately the probability that there are more than B/P bursts in the infinite queue system (M/M/1), so we have B

CLR = Pr{ Cell Buf fer Overf low } ≈ ρ P

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The probability that there are n packets in the M/M/1 system is

P n = Pr{ n packets in M/M/1 system } = ρ n ⋅ (1 − ρ ).

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Cell Buffer Overflow Analysis

Simulations are time consuming and, in the case of modeling complex high-speed technologies like ATM, cannot effectively model low cellloss rates since an inordinate number of cells must be simulated.

Buffer Overflow and Performance 



Traffic Engineering Complexit ies 


Buffer overflow probability in frame and cell networks increases as the higher layer PDU sizes ( P value) increase. Therefore, the required buffer size for achieving an objective CLR is approximated by B ≈ P ⋅ log ρ CLR. The shared output buffer scheme has a marked improvement on buffer overflow performance because of sharing a single, larger buffer among many ports, and it is unlikely that all ports are congested at the same time.



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Switch Buffering Performance



Overflow Probability versus PDU Burst Size

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Shared versus Dedicated Buffer Performance

Statistical Multiplexing Gain 

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Statistical multiplexing attempts to exploit the on/off, bursty nature of many source types. As more and more sources are multiplexed, the statistics of this composite sum become increasingly more predictable. Statistical

G=

Multiplex Gain

≈

Number of Sources Su pported Required N umber of C hannels

[

N α 2 (b − 1) + 4b η − α b − 1

]

2

4

N: the number of sources; Q( ): the objective cell loss ratio (CLR); b: the burstiness (peak/average rate); : the peak source-rate-to-link-rate ratio. 



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The rate of any individual source should be low with respect to the link rate η , and the burstiness of the sources b must be high in order to achieve a high statistical multiplex gain G .



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Illustration of Statistical Multiplex Gain



Achievable Statistical Multiplex Gain

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LAN Traffic Modeling 

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Statistical Multiplex Gain Example



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LAN traffic modeling is a difficult process and is subject to further study. Many LAN traffic characteristics, such as performance and throughput based on number of users, have been thoroughly studied and provided by equipment vendors. The Token Ring throughput increases when the number of users increases because less time is spent token passing, whereas on the Ethernet (CSMA/CD) LAN the throughput decreases as the number of users increases due to the increased likelihood (and number) of collisions. LAN bridge designs are concerned primarily with frames forwarded per second and frames filtered per second. Packet and frame forwarding and filtering buffers, as well as the depth of LAN address table memory, should also be considered in the designs.



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LAN Throughput Comparison

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DQDB (Distribute Queue Double Bus) MAN Traffic Modeling 

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The DQDB bus operates as a LAN, but handles calls similarly to the Erlang method, where messages contending for the bus have to wait until they can reserve a space on the bus. The required capacity of a DQDB MAN to handle all user traffic is calculated with the sum of the λ’s (packets/sec) of the local, remote traffic from and to the MAN, and the pass-through traffic, i.e.

∑ λ + ∑ λ + ∑ λ L

RF

RT

= λ '

,

where all λ’s are the sum of the users in that category and λ’ represents the minimum required capacity of the local MAN. 



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Since MANs often provide high-bandwidth connectivity to a small number of users, the traffic approximations just discussed become valid (where aggregations tend to have Poisson distributions). Huge bursts on the MAN can dwarf the normal large packet transmissions normally seen on the LAN.




Characteristics of LANs Attached to MANs

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The probability that a LAN time slot will be busy is given by

ρ B = 

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 

Avg . number of slots con sumed by burst n

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t b t s ⋅ n

=

8 ⋅ Db B ⋅ t s ⋅ n

=

λ μ

The average utilization (or throughput) of the DQ DB MAN can be approximated by μ M = N ⋅ ρM y (assume there are N LANs connected to MAN and the MAN is y times faster than each LAN). The average M/M/1 delay is proportional to 1 (1 − μ M ) . Queueing power is defined as the ratio of throughput to delay, i.e.

Throughput

= μ M (1 − μ M ) =

N ⋅ ρ M

⎛ N ⋅ ρ M ⎞ − ⎜⎜ ⎟⎟ ⎝ y ⎠

2

Delay y The optimum number of LANs that can be connected to the MAN can be solved as ∂ P y

∂ N EE5302 Network Design and Management

=

The probability that a LAN will transmit onto a particular MAN slot is ρM = ρ B ⋅ ρ InterLAN ( ρ InterLAN : the fraction of inter-LAN bursts).

P =


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= 0 ⇒ N opt =

2 ⋅ ρ M


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Assume the user has the entire bandwidt h of each link, the total delay is given by 

2 Mbit 128 kbps

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⋅ (4 + 2 − 1) = 78.125 sec .

Impact of Delay on Application 

In packet-switching , the total message delay is calculated as

p c

⋅ (n + 1) +

m − p



c

where p: packet size; c: transmission rate of the medium; n: number of intermediate nodes; m : message size. Example: if the 2 Mbit file is transmitted over the four-node (not counting the origination and destination nodes) network with 56 kbps trunks, using packet sizes of 1024 bits, the total delay in transmitting the entire file is 

1024 56000

⋅ (4 + 1) +

1998976 56000

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= 35.79 sec .

Cell-switching delay best resembles packet-switching delay. Data exceeding the available throughput is discarded, with no retransmission.



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A latency-limited application occurs when the transmitter finishes sending the burst of data before the receiver begins receiving any data. The latency of the response from the receiver limits additional transmission of information. By applying the basic M/M/1 queueing theory, the average M/M/ 1 queueing-plus-transmission delay in the network is b b: burst length; R : peak transmission rate of the network; : average trunk utilization. R ⋅ (1 − ρ ) The point where the average queueing-plus-transmission delay exactly equals the propagation delay is called the latency /bandw idth crossover point .



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Propagation Delay, Burst Length, and Peak Rate


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A bandwidth-limited application occurs when the receiver begins receiving data before the transmitter has completed transmission of the burst. The lack of bandwidth to hold the transmission limits the transmitter from releasing the entire message immediately.

Latency/Bandwidth Crossover Point

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Traffic Matrix

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Interpreting the Matrix 

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Traffic Engineering and Capacity Planning 12. Capacity Planning and Network Vision 

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A short-term objective and task-oriented plan is usually revised each year, and the long-term 3- to 5-year plan should also take into account the strategic vision of corporate communications for the next 5 to 10 years. Both plans must take into account the business needs, customer needs, and the technologies. As the cost of computing hardware decreases, the entropy of capacity requirements increases. This makes capacity planning for communications networks a challenging task. Short- and long-range capacity planning can provide the corporation with a competitive advantage, assuming that the plans fit in with the long-range strategic business plan. Each design must provide the flexibility to change technology as business needs change without major network restructuring. One of the best architecture is that which is built in a hierarchical nature that caters to flexible p eer-to-peer communications and can be replaced in layers over time.



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In its simplest use, the matrix shows the connectivity required between each site. All traffic identified as remaining local to a given node would be placed into the same access node. Group nodal traffic distributions in a local geographical area together to form larger access nodes. The small amount of traffic originating at some nodes is “backhauled” to the regional concentrator. This process continues until the number of access nodes required to begin the design is established. Network designs for multimedia and multiprotocol networks are much more complicated. These designs often require many traffic matrices combined into a multidimensional matrix (e.g. a z-axis to represent priority or protocol), or in large networks, desig n tools to perform these calculations.



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Traffic Engineering Capacity Planning

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