International Journal of Advanced Computer Science, Vol. 2, No. 10, Pp. 389-393, Oct., 2012.

Manuscript Received: 27,Dec., 2011

Revised:

18,May, 2012

Accepted:

16,Jul., 2012

Published: 15,Nov., 2012

Keywords

Traffic shaping,

traffic policing,

Hurst

parameter,

self similarity,

autocorrelation

function,

variance

Abstract Aggregate traffic in high

speed networks is characterized by “burst

within burst” structure over a wide range

of time scales, heavy tailed inter-arrival

time densities, self-similarity and long

range dependence. These properties are

followed by a degradation of quality of

service in aggregate traffic. A high

self-similarity grade will eventually fill up

the network equipments memory and

queues no matter how large they are

designed. This paper compares the effect of

traffic shaping and traffic policing on

aggregate traffic dynamics especially

stochastic properties on traffic time series.

We propose a series of tests and compare

the result obtained from OPNET Modeler

simulation program to simulation with

synthetic traffic generated by chaotic

dynamic systems. We are interested in

parameters such as Hurst parameter,

autocorrelation function and variance for

different shaping and policing techniques

applied to input traffic in order to obtain

smooth aggregate traffic trace.

1. Introduction

Many applications and services in communication

networks generate high variable data rates that create the

phenomena called “burst within burst”. This type of traffic

has no impact on network performance as long as the

network can guarantee end-to-end bandwidth large enough

to support all traffic patterns, but from experience we know

that this is impossible because more bandwidth means more

costs. High data rates and burst phenomena are the effects

of software such as bittorent and their purpose of

distribution large amount of information (music, movies,

software, etc) among others. Those applications and

services generate aggregate traffic characterized by

self-similarity, long range dependence and heavy tail

inter-arrival time densities which are responsible for delays,

delay variation and bottleneck.

Studies of internet traffic [1,2] show the growing

fraction of such bulk transfer across internet. For limiting

this properties network engineers can use traffic shaping

and traffic policing. It is demonstrated by [3] that rate

limiting bulk transfers results in substantial peak bandwidth

reduction resulting lower costs. Rate limiting can be

achieved by shaping or policing the traffic.

In today’s unified communication network class based

traffic shaping or traffic policing is used for classification of

different applications in service classes (data class, voice

class, video class, etc), guarantee for service class

parameters such as delay, bandwidth, priority, and shape or

polish all aggregate traffic. In this way aggregate traffic data

rate specified by network administrator is achieved (maxim

throughput is imposed), services as voice or video are

prioritized and allocated to queues with higher priority and

bandwidth, and data traffic is smoother. The question is

how varies the degree of self-similarity and burst of the

aggregated traffic in WAN if multiple input data rates are

shaped or policed before entering the network core or WAN

(Wide area network).

2. Simulation Methodology

Hub and spoke topology from figure 1 is used for tests

because it is commonly deployed in communications where

different remote locations communicate with a central

location and the access from spoke locations in other

networks (internet) is always through central location.

Fig 1 Network topology studied

The link between HUB and switch simulates WAN.

Each spoke represents remote locations LAN connected to a

gateway router on which we configure traffic shaping and

policing for outgoing traffic.

The simulations are based on generating traffic from

remote locations to the central servers and compute Hurst

parameter for the aggregate traffic between HUB and

Switch. The second step is the configuration of traffic

shaping and traffic policing in each remote location router

for upload traffic and re-compute Hurst parameter. Hurst

parameter is a measure of traffic characteristics related to

burst, self-similarity, delay with values between 0 and 1.

Greater value means more delay and jitter for the aggregate

network traffic.

The main problem for simulation is the generation of

synthetic traffic that can match the range of real packet

Traffic Shaping and Traffic Policing Impacts on

Aggregate Traffic Behavior in High Speed Networks Dăian Daniel-Simion

International Journal of Advanced Computer Science, Vol. 2, No. 10, Pp. 389-393, Oct., 2012.

International Journal Publishers Group (IJPG) ©

390

traffic behavior. We chose to utilize OPNET Modeler for

traffic generation and traffic policing. Topology from figure

1 is implemented in OPNET Modeler. Traffic generation is

based on voice, ftp and web traffic generated from remote

LAN to central servers. Traffic policing is implemented by

rate limiting upload traffic on spoke routers. Next Matlab is

used for traffic shaping and Hurst computation. OPNET

synthetic traffic is compared with synthetic traffic generated

offline with dynamic chaotic systems (chaotic maps). It is

demonstrated by [4,5,6] that chaotic maps can match the

properties observed in real traffic trace from today’s

networks.

We use as synthetic traffic generator Matlab

environment and the double intermittency map system, as

mathematical model, described by equations Equ.1 and 2.

The generator equations are described in Equ. 3, 4 and 5.

1111 )(

mxcxxf ,

1m

11

d

dε1c

(Equ. 1)

2)1()( 222m

xcxxf ,2)1(

22 m

d

edc

( Equ. 2)

The traffic generation process yn is given by the

iteration of xn as described below.

1),(

0),(

)()(2

)()(1

1)(

ni

ni

ni

ni

ni

xdxf

dxxfx , 0<d<1 (Equ. 3)

1,1

0,0

)(

)()(

ni

ni

ni

xd

dxy (Equ. 4)

where i=1,2,…N is source index; n=1,2,… is time index.

Each LAN in our topology consists of 10 workstations

thus the synthetic traffic generated by chaotic map is

defined by the aggregation of N=10 sources as the equation

below.

N

ni

n yky1

)( (Equ. 5)

N represent the number of sources and k is the number

of packets (bytes) generated by each source at every

iteration “i”. We use this model because of simplicity and

the fact that it permits the generation of heavy tail traffic

with self-similarity by only varying a few variables.

3. OPNET Simulation Results

In the first scenario we generate synthetic traffic from

remote LAN to the FTP, HTTP and VoIP servers with

OPNET Modeler. Figure 2 shows the average throughput

and Hurst parameter value for upload traffic from HUB to

central Switch with and without rate limit of traffic

generated by remote locations router.

Policed traffic trace is smoother and the throughput

remains under the traffic contract specified by rate limit

configuration on remote LAN routers. We limit each upload

Fig. 2 OPNET HUB to Switch upload traffic trace: Blue trace is without rate limit, Hurst parameter equal to 0.845, red trace is 1125 kbps rate limit

of upload traffic from remote LAN, Hurst parameter equal to 0.853 and

green trace is 512kbps rate limit of upload traffic from remote LAN, Hurst parameter equal to 0.85.

traffic throughput from remote locations to an average

512kbps and 1100kbps rate.

Hurst parameter was estimated by Bill Davidson and

Variance - Aggregation method. From rate limit we obtain a

reduced peak rate but with no changes in self-similarity

degree. Policing will forward bursts and further aggregation

of this traffic in high speed networks can produce increased

latency and jitter on links sub dimensioned or congested.

At this moment we are not interested in latency or

jitter because class based traffic policing permits

classification and guarantee of named parameters for

selected traffic.

In the second scenario we use generic traffic shaping

model to shape the upload traffic from remote LAN

locations and aggregation model from Equ.5 to construct

traffic trace from HUB to Switch. Matlab environment is

used in offline computation and simulation. Traffic traces

used in this scenario are the same traffic traces generated in

the first scenario in OPNET Modeler from remote routers to

central HUB and they are used offline in Matlab

environment. Figure 3 present the average throughput and

Hurst parameter value for upload traffic from HUB to

central Switch without and with generic traffic shaping

applied on output of WAN interface for each spoke router.

Fig. 3 OPNET HUB to Switch upload traffic trace: Blue trace is without

shaping, Hurst parameter equal to 0.835, red trace is 1125 kbps shaped of upload traffic from remote LAN router, Hurst parameter equal to 0.843.

In figure 4 is detailed upload traffic from spoke 1

Dăian Daniel-Simion et al.: Traffic Shaping and Traffic Policing Impacts on Aggregate Traffic Behavior in High Speed Networks.

International Journal Publishers Group (IJPG) ©

391

router to HUB central location without and with generic

traffic shaping.

Fig. 4 OPNET Spoke 1 Router to HUB upload traffic trace: Blue trace is

without shaping, Hurst parameter equal to 0.825, red trace is 1125 kbps

shaped of upload traffic from remote LAN router, Hurst parameter equal to 0.838.

There is no modification in aggregated traffic behavior

by shaping input traffic and further aggregation of this kind

of trace will produce self-similarity and high value of Hurst

parameter. We run simulation for different average shaping

values but the effect was the same related to the Hurst

parameter. The only change was found in the peak data rates

because shaped traffic offers a long term average data rate

equal with the average shape value but with higher memory

and CPU utilization for routers on witch shape process is

configured.

Even if shaped trace seems to be smoother when we

look at it, we can’t obtain smaller values for Hurst

parameter. In conclusion by implementation of shaping and

policing techniques only quality of service and service level

agreement can be achieved without any affect on

self-similarity and bursts. The last two phenomena’s are still

present in our networks.

Fig. 5 Variance-time plot of upload traffic from HUB to Switch for

different rate limit applied to input traffic from remote LAN: Green trace is

for 512kbps average rate limit, red trace is for 1125kbps average rate limit and blue trace is initial traffic trace without rate limit.

Another test for verifying self-similarity is the

property of slowly decaying variance. For self-similar

process the variance of a sample decreases very slowly,

much slower than sample size. Figure 5 present the

variance-time plot of the upload traffic from HUB to switch

for different rate limit applied to input traffic from remote

LAN. The slope of the variance decay much slower than

sample size.

The variance variation of initial HUB to switch upload

traffic and shaped traffic versus sample size is presented in

figure 6. Shaped traffic means generic traffic shaping

applied to LAN traffic on LAN router outside interface and

aggregation in HUB central router. Upload aggregated

traffic ins observed and named shaped traffic.

Fig. 6 Variance-time plot of upload traffic from HUB to Switch for generic

traffic shaping applied to input traffic from remote LAN: blue trace is variance for traffic without shaping and red trace is variance for generic

traffic shaping applied to input traffic.

The traffic self-similarity property can be observed

by taking a look to autocorrelation function. For most

process the autocorrelation function decays rapidly to

zero, sometimes exponential. Self-similar processes are

characterized by autocorrelation function that drops very

slowly to zero, sometimes never reach zero.

Fig. 7 Non self-similar process autocorrelation function

Fig. 8 Autocorrelation function of HUB to switch upload traffic: black

trace is for the initial traffic, blue trace is for shaped traffic, red trace is for

policed traffic.

International Journal of Advanced Computer Science, Vol. 2, No. 10, Pp. 389-393, Oct., 2012.

International Journal Publishers Group (IJPG) ©

392

Figure 7 and 8 present the autocorrelation function

for a non-self-similar process and autocorrelation

function for upload traffic from HUB to switch with and

without rate limit and shape.

4. Matlab Simulation Results

Furthermore we use traffic generated from chaotic

map, described by Equ. 1 and 2, for comparison with

OPNET Modeler traffic trace. In Matlab we generate four

traces corresponding to remote LAN upload traffic. Those

traces were shaped and then aggregated to simulate HUB

upload traffic to central switch. For each stage Hurst

parameter was computed to observe the effect of shaped

input trace into WAN aggregate traffic.

Figure 8 show aggregate traffic trace with and without

generic traffic shaping applied to four different input

sources. Traffic generated from each input source has

chaotic systems as mathematical model.

Fig.9 MATLAB synthetic traffic trace: Blue trace is offline synthetic

aggregated trace from four traffic sources with Hurst parameter equal to 0.871 and red trace is the aggregated trace from four shaped traffic sources

with Hurst parameter equal to 0.883.

Each input source was shaped and the resulted traffic

aggregated to obtain aggregate traffic pattern colored red in

the figure 8. In this case we observe the same situation

where shaping individual traffic sources do not reduce the

self-similarity degree of the aggregate traffic.

Further aggregation of this type of traffic will produce

self-similarity and heavy tail with high value of Hurst

parameter.

Figure 9 present the aggregate traffic behavior for rate

limited input traffic. Each input traffic source was rate

limited and aggregate traffic computed. There are no major

changes in self-similarity. Figure 10 present autocorrelation

function for Matlab traffic traces, initial, shaped and policed

trace.

Fig. 10 MATLAB synthetic traffic trace: offline synthetic aggregated trace

from four policed traffic sources with Hurst parameter equal to 0.869.

Fig. 11 Autocorrelation function for synthetic traffic trace: black trace is for the initial traffic, blue trace is for shaped traffic, red trace is for policed

traffic.

5. Conclusion

Traffic shaping and traffic policing can only reduce the

data rates but have no impact on aggregate traffic behavior.

Further aggregation will maintain “burst within burst”

phenomena in traffic trace time series. With this observation

made the next generation network must deploy new

aggregation models as data rates moves toward 100

Gigabytes per second per data port. New aggregation

methods must rearrange data to minimize self-similarity and

heavy tail without degradation in the quality of service.

For the moment we can’t escape from observed

phenomena in network traffic but we can try further to

understand it and to deploy networks designed to support

this kind of traffic. It is very difficult to predict network

traffic but chaos theory help us to understand and control

this kind of phenomena. A realistic network traffic

prediction e.g. [7] means lower costs and fewer problems.

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Dăian Daniel-Simion et al.: Traffic Shaping and Traffic Policing Impacts on Aggregate Traffic Behavior in High Speed Networks.

International Journal Publishers Group (IJPG) ©

393

[5] Pruthi P., Erramilli A., Heavy-Tailed ON/OFF Source Behaviour and Self Similar Traffic, Proc. ICC 95, June 1995.

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