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Ubiquitous Computing and Communication Journal 1

DYNAMIC RESOURCE MANAGEMENT APPROACH INQoS-AWARE IP NETWORKS USING FLATNESS BASED

TRAJECTORY TRACKING CONTROL

Lynda Zitoune, Amel Hamdi, Hugues Mounier, Vronique VqueInstitut dElectronique Fondamentale, Bt 220

Unviversit Paris Sud XI, 91405 Orsay cedex, France

[email protected]

ABSTRACTIn this paper, we present a reactive control policy which adapts the source bit rate to the

reserved resources in order to ensure performance guarantees for multimedia applications.

The proposed method called flatness based trajectory tracking deals with drastic traffic

flow rate changes and limits the traffic in order to respect the time constraints. We show

the contribution of the reactive control and the dynamic regulation using purely control

theoretic approaches which stabilize the network and avoid undesirable oscillations forthe transmission of such critical flows. Here, we present a performance analysis for such

rate control mechanism, and illustrate its feasibility through its implementation on

MPLS-TE control plane of SSFnet/Glass simulator.

Keywords: QoS, traffic engineering, rate regulation, trajectory tracking appproach.

1 INTRODUCTIONThe growth of multimedia applications over

wide area networks has increased research interest in

QoS (Quality of Service). Such applications need to

find appropriate and available network resources andreserve them in order to support and guarantee their

specific services. For instance, MPLS-TE (Multi-

Protocol Label Switching, Traffic Engineering TE)

[1] intends to balance load over multiple paths in the

network to minimize its congestion level. It isperformed using QoS routing algorithms in order to

meet QoS constraints, while at the same time

optimizing some global, network-wide objective

such as utilization [1], [2].

However, MPLS-TE provides no per-flow QoS

guarantee [2], [3]. We claim that QoS routing is not

enough in itself to guarantee accurate QoSrequirements. QoS routing is able to provide some

high level granularity of performance when traffic

load is stationary. But, in case of high throughputvariation, data flows can be affected by longer

queuing delays or at worst by losses.

So, we must resolve the congestion and service

degradation problems where they occur, i.e., at the

routers queues. We use packet flow control andqueue management operations that control the buffer

occupancy, same as the conditioning functions of

DiffServ (Differentiated Service) [4] token bucket

approach [5]. However, most developed works

conclude that token bucket shaping can experience

unpredictable delays, packet reordering, and even

losses when congestion occurs [6], [7], [8].

Therefore, to improve services offered to

applications, we propose to add a flow control

function to adjust the traffic arrival according totraffic specification to achieve QoS requirements.

In this paper, we present a mathematical

framework for rate regulation (adaptation) of

multimedia applications, with the aim of matchingQoS requirements and maximizing network resource

utilization. We use a nonlinear approach of

theoretical control named flatness based trajectory

tracking and develop a reactive and an adaptive

control method which stabilizes general networkbehavior, improves network resource utilization and

ensures delay transfer requirements.

The rest of the paper is organized as follow. In

section 2, we give the main motivations for

developing a reactive packet streams control. Section3 explains the functionality of our trajectory tracking

control. Section 4 is devoted to the flatness

methodology and to the implementation of the

approach based on our target environment. In section

5, we present the simulation results and their analysis

that illustrate feedback control by the trajectorytracking approach. Finally, section 6 summarizes our

rate analysis findings and concludes the paper.


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2 MOTIVATIONS AND RELATED WORKS2.1 Traffic Engineering Tools

Many architectures and mechanisms have been

proposed by the IETF (Internet Engineering Task

Force) for enabling QoS, such as IntServ (Integrated

Service) [9], DiffServ (Differentiated Service) [4],[10] and MPLS-TE (Multi-Protocol Label Switching,Traffic Engineering) [1], [11]. Two QoS issues are

mainly addressed and developed using Traffic

Engineering (TE) functionalities: resource allocation

and performance optimization.

To achieve Traffic Engineering goals, theutilization of the network resources is optimized by

the Traffic Engineering process periodically. As

defined by TEWG (Traffic Engineering Working

Group) of IETF, the process of traffic Engineering

consists of measuring, characterizing, modelling and

control of the network resources. It encompasses thereliable expeditious movement of traffic through the

network, the efficient utilization of network

resources, and the planning of network capacity [1],

[11]. In other terms, the main objective of Traffic

Engineering is an efficient mapping of traffic

demands onto the network topology to maximize

resource utilization while meeting QoS constraints

such as delay, jitter, packet loss rate and throughput.

Using QoS routing mechanisms such as multi-

path routing, MPLS-TE avoids link saturation and

achieves some link optimization. The general

schematic of multipath routing algorithm consists oftwo steps: computation of multiple candidate paths

and traffic splitting among these multiple paths like

CBR Constraint-Based Routing (as summarized in

[2], [3]. Each multi-path routing mechanism in the

literature is declared very efficient by its authors but

generally with restricted conditions [3]. A new

routing paradigm that emphasizes searching for

acceptable paths satisfying various QoS requirements

is needed for integrated communication networks.

QoS routing of MPLS-TE is a coarse-grained

solution that offers many advantages to service

providers. However, it provides no per-flow QoSguarantee. So, we must resolve the congestion and

service degradation problems where they occur, i.e.at the router's queues.

The network optimization process is performed

with three temporal resolution levels [11], [12]. The

long term: as the network evolves with traffic

demand growth, capacity management and planning

are required to meet the traffic demands. In

intermediate-time-resolution, QoS routing

mechanisms are used as an important tool for

resource control as mentioned earlier. The short time

resolution level: some traffic engineering methods

such as queue management, conditioning and

scheduling at switches and routers to deal withproblems of congestion and service degradation.

Token bucket conditioning approach checks the

packet flow compliance with respect to the

negotiated contract, by smoothing exceeding flow(policing and shaping) [5]. The token bucket is asource descriptor in terms of burst and mean rate. It

gives a simplified model of the sources though it is

not faithful to actual behaviour. The most advance

works concerns the definition and the adaptation of

the token bucket parameters [5], [6], and [7] in order

to shape input traffic in accordance with negotiatedtraffic profiles. It is seen that token bucket shaping

can experience unpredictable delays, packets re-

ordering and even losses when congestion occurs (as

demonstrated in [5], [6], and [8]. Using token bucket

control, no deterministic service can be provided in

the network.

As a conclusion, we believe that token bucket is

not a service guarantee mechanism because of two

main drawbacks: 1) it is independent of router's

buffer state, since token bucket parameters are

specified based on a priori source models. 2) It is an

open loop control which does not consider the input

rate variations, i.e. the source bit rates during

transmission.

On the other side, a numbers of researchers have

developed solutions to dynamically control the video

bit-rates depending on available network bandwidth.Most of these solutions adopt TCP protocol for

transporting video stream. They use its feedback

loop control information to estimate network state,

and subsequently determine the bit-rate to be used

for converting and transmitting the video stream [13]

So, to provide end-to-end QoS support over the

network, we propose to add packet flow control to

TE short term process. Unlike TCP feedback controlused to adapt video stream, this study focus on

developing dynamic bit-rate adaptation approach to

enforce the network resource planning which is

performed at the long term resolution level. We useflatness based control theory to develop this new

monitoring method.

2.2 Why Feedback ControlAlthough steady-state characteristics can be well

understood using queuing theory (e.g., as is done

with capacity planning). Here, we use flatness based

control theory to address dynamics of resource

management, especially changes in network

workloads and configuration.

In other words, we are interested in designing a new

network resource monitoring approach in order to


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ensure performance characteristics of critical

applications in MPLS-TE networks, especially delaytransfer, bandwidth and queue's lengths. We consider

network metric like queue length, as measured

output and the traffic requirements or profiles as

inputs.

The control is performed by adjusting the

network inputs, i.e. applications input rates which

affect buffer sizes with respect to the buffer planning.

The need for regulatory control arises first to track

the reserve capacity, and second to enforce the

application service guarantees.

Since the measured outputs are used to

determine the control inputs, and the inputs then

affect the outputs, the mechanism is called feedback

or closed loop. Feedback control based on flatness

notion is a powerful tool 1) to ensure that the

measured output (buffer utilization) tracks veryclosely the reference input (buffer planning) even in

the presence of disturbances, 2) to deal with network

stability and other aspects of control performance,

especially when changes in network workloads andconfiguration occur.

2.3 Our Work ObjectiveDepending on network variations, we use a

feedback mechanism to inform the sources when

they exceed their profile and regulate their input

rates in order to match the reserved network resource

and to meet their QoS requirements. As a result,

dynamic adaptation is provided between clients and

their service provider.

The purpose of this work is to present this new

Traffic Engineering approach, which aims to

optimize the network utilization and performance by

intelligently handling the buffer reservations at the

routers. We take benefit of traffic descriptors to

model communication behaviours and QoS

requirements by using trajectories.

Trajectory would be a new way of mapping traffic

demands over networks. The trajectory establishment

or network resource planning translation into

trajectories is out of the scope of this paper. Here, weare interested mainly in source bit rate regulation to

meet QoS requirements and bounding packet delay.

3 RATE REGULATION SCHEMEOur method called Flatness-based trajectory

tracking control is a network-driven intra-domain

and inter-domain layer 3 bandwidth provision

approach. We aim to prove its efficiency, when

applied at the hotspot nodes such as the ingress

nodes of large scale networks like Internet, and also,

the nodes that aggregate flows of several LSPs

(Label switched Path), in case of an MPLS-TEdomain (as in figure Fig.1). Network resources

considered here are buffers and bandwidth. Our

controller monitors the router queue state accordingto the traffic requirements and regulates the

incoming bit rate in a smooth manner. Indeed, the

delivered service is more reliable and predictable

with regard to network performance.

Classically, most papers consider the problemfrom the network dimensioning point of view: given

an input stream and a scheduling policy, what is the

worst case buffer requirement and what is the nature

of the output stream? However, in our case the

output stream and the buffer size are given and

described by the trajectory. So, we the input streamto meet buffer constraints and to maintain QoS. We

use a nonlinear theoretical approach to resolve this

reverse problem, named flatness based trajectorytracking control [14], [15], [16].

In the following, we first state the problemdefinition, and then briefly recall flatness notion and

expose the trajectory tracking control method with

reference to the target environment of figure Fig.1.

The packet stream is processed and controlled at the

node denotedRc and, the router resources considered

here are the buffer size and the bandwidth.

3.1 Trajectory Tracking Control MethodologyQoS control requires an understanding of the

quantitative parameters at the application and

network layers. For example, from a traffic

descriptor which describes the requested service, we

deduce what will be the node buffer variation of thecorresponding LSP and we define the buffer

trajectory, denoted as qref(example in section 5.1).

Recall that we assume a MPLS-TE architecture.

For each incoming stream an LSP is established and,

a buffer queue is created to support the

corresponding packets. The critical router Rc of the

figure Fig.1, collects the packets generated by n

earlier routers in its buffer q(t). The incoming ratesof these packets are denoted ui(t). The aggregated

packets are served with some service bit rate r(q(t))

to the next hop following their LSP. IfRc is not able

to handle all the incoming packets, the packets areeither buffered to wait for transmission service or

rejected in case of buffer saturation. The lack offeedback between adjacent routers may cause an

excessive data loss and bad transmission service

(delay violation) for these critical flows.


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Figure 1: Target environment of the developed work

Our objective is to control the input streams bit

rates ui(t) in order to meet the buffers, reserved in

advance, at the routerRc and which we have modeled

by a trajectory denoted by qref. Thus, the feedback

control adjusts the ui(t) so that the packets sent by theclients are accepted at the router queue q. In other

terms, the controller ensures that q(t) tracks qref(t), in

order to maximize the utilization of the reserved

resources; especially during transitions between

lack/availabilty of buffers, to match the QoS

requirement like packet delay and, to avoid bit rateoscillations and excessive loss.

QoS is of particular concern for the continuous

transmission of high-bandwidth video and

multimedia traffic demands. We have used a fluid

flow model to represent such bulk data transfer.

3.2 Flatness notion brief RecallLet us briefly recall the flatness notion for

systems with a statex and controls u [14], [15]

Definition 1. The system

),( uxfx =&

withn

Rx andm

Ru is differentially flat ifthere exists a set of variables, called a flat output,

),,,,( )(ruuxhy L= ,mRy Nr

Such that

),,,(

),,,(

)(

)(

u

x

yyyBu

yyyAx

L&

L&

=

=

With an integer, and such that the system equations

)),,,(

),,,,((),,,(

)1(

)()1(

+

+=

yyyB

yyyAfyyydt

A

L&

L&L&

are identically satisfied.

The preceding notion will be used to obtain an open

loop control; that is control laws which will ensure

the tracking of the reference flat outputs when the

model is assumed to be perfect and the initial state

conditions are assumed to be exactly known. Sincethis is never the case in practice, one needs somefeedback schemes that will ensure asymptotic

convergence to zero of tracking errors.

Our framework can thus be decomposed into two

steps:

1. Design of the reference trajectory of the flatoutputs; off-line computation of the open

loop controls.

2. Inline computation of the complementaryclosed loop controls in order to stabilize the

system around the reference trajectory.

This two steps design is better than a classical

stabilization scheme. The first step obtains a first

order solution to the tracking problem, while

following the model instead of forcing it (like in a

usual pure stabilization scheme). The second step is

refinement, where the error between the actual

values and the tracked references is much smaller

than in the pure stabilization case (see [14], [15],

[16]).

4. Trajectory Tracking Control ImplementationIn the fluid flow paradigm, the physical evidence

is that the rate of packet accumulation in the buffer is

the difference between the packets inflow rate and

the packets outflow rate. So for the model depicted

in figure Fig.1, we obtain a differential equation

describing the queue length variation of routerRc

=

=

n

i

ii tqrhtutq1

))(()()(& (1)

where hi is the delay between Rc and the previous

hop.

The positivity of the buffer queue length as well

as its maximum capacity are considered by

describing the outflow rate r(q(t)) in terms of the

contents of the buffers q(t) (see [6] ).

We take)(

)())((

tqa

tqtqr

+= which is (as

demonstrated in [17]) a positive bounded function of

the load q and a monotonically increasing one. The

parameter may be interpreted as the maximal

processing capacity of the router. This relation is


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obtained by assuming a linear relation between the

residence time (queuing delay) and the buffer queuelength. In case ofM/M/1 queue, a=1 (see [17] for

more details).

To determine the input controls ui(t) of equation

Eq.(1), we proceed as follows. We have consideredthat the router is composed ofn virtual files (q1(t), ,qn(t)) which accumulate the packets coming from the

n former hops respectively.

These stored packets are released to the true file q

(see figure Fig.2). The router output service rate may

be expressed as

=

+

=n

i

i

ii

tq

tqtqr

1

)(1

)())(( .

Where i are weights for service scheduling of the n

flows, with 1

1

=

=

n

ii . If the aggregated flows are

requesting for the same QoS, we chooseni

1= to

ensure fairness between their competing packets.

Figure 2: Virtual model at router sideRc

The virtual model corresponding to the physical

model (Fig.1) is treated as a composition of n

differential equations describing the virtual queue

variations (q1(t), , qn(t)), summarized as:

=

+

= n

itq

tqhtutq

i

iiiii

1)(1

)()()( & (2)

As cited earlier (section 3.2), our framework is

decomposed into two steps: 1) off-line computationof the open loop controls. 2) Inline computation ofthe complementary closed loop controls to stabilize

the system around the reference trajectories.

4.1 Flatness based control: open loop controlThe model described by the Eq.(2) is flat with

qi(t) as a flat output. In other words, we get a

complete parameterization of the system in terms of

qi(t) and of a finite number of its derivatives. Thus,

ui(t) is a nonlinear expression of qi(t) and its

derivatives, as explicitly demonstrated below in

Eq.(3):

=

++

+++=

n

ii

iiiiii

ihtq

htqhtqtu

1)(1

)()()( & (3)

Thus, for some reference trajectories qiref(t) of the

reserved buffers, the former nodes output bit rates

are defined by equation Eq.(4):

=

++

+++=

n

iiref

iiref

iiirefi

ihtq

htqhtqtu

1)(1

)()()( & (4)

Which ensures the open loop tracking ofqiref(t).

4.2 Flatness based control: closed loop controlWe now illustrate how to compute the closedloop control which ensures the tracking of the router

buffer size qi(t) to the reference trajectory qiref(t),

when the system becomes unstable. This is done by

computing tracking error ei(t) such that ei(t) = qi(t) -

qiref(t).

The feedback control law is computed in order to

minimize the closed loop error dynamics such that

ei(t) = - Kiei(t), so

))()(()()( tqtqKtqtq irefiiirefi = && (5)

Replacing )(tqi& from equation Eq.(2) in equation

Eq.(5)), we have

))()((

)(

)(1

)()(

1

tqtqK

tq

tq

tqhtu

irefii

irefn

i

i

iiii

=

+

=

&

(6)

)(

)(1

)()()(

1

iiref

n

i

ii

iiiiii

htq

htq

htqhteKtu

++

++

+++=

=

&

(7)

So the closed loop control given by the equation

Eq.(7) ensures )(tqiref tracking when instabilities

occurs

This dynamic control approach is extended to

consider end-to-end QoS support over MPLS-TE

networks (see [18] for more details).


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5. SIMULATION AND RESULTS

5.1 Simulation ScenarioHere, we present our trajectory tracking

controller as we have implemented under SSFNet

(Scalable Simulation Framework)/Glass (GMPLS

Lightwave Agile Switching Simulator). SSFNet[19]is a collection of Java components for modeling andsimulation of Internet protocols and networks at and

above the IP packet level. Link and physical layers

modeling can be provided in separate components,

like Glass [20] which is a simulation engine that

allows the modeling and performance evaluation of

routing, restoration, and signaling protocols foroptical networks.

We have made two major modifications. Our

controller is implemented as a new AQM (Active

Queue Management) method at the interface level.

The monitoring is done at intervals using timers. Foreach interval, the controller compares the queue

occupancy, qsize, to the number of reserved buffers

qref. Based on the difference, it computes the new

input bit rate (using the discrete version of equation

Eq.(7)), that the sender must use to release its stream.

We have use a predictor (using standard Euler

prediction) for estimating network variation to

consider transfer delays hi.

For signaling, we have added a new notification

message for CR-LPD (Constraint-based Label

Distribution Protocol) of Glass Simulator, to notify

the LERs (Label Egress Router) of the new input bit

rate.

The trajectory tracking controller is tested on the

scenario depicted in figure Fig.3.

Figure 3: Simulated network topology

Our network simulation implements a MPLS-TE

architecture that configures ingress routers (LabelEdge Router LER), core routers (Label Switch

Router, LSR), and the 4 corresponding LSPs that

connect clients to servers respectively (Si, Ci). The

link connecting LSRs (Label Switch Router) 220 and

221 is the critical one, because it must support the 4

LSPs. So, we have implemented four trajectory

tracking controllers. Two at the LSR 221 to handle

LSP1 and LSP3, connecting (S1 , C1) and (S2 , C2)respectively. The others at LSR 220 to control LSP4and LSP6, connecting (S3, C3) and (S4, C4)

respectively. These controllers regulate the output

rate of LERs 211, 213 and 210, 212 respectively. For

these controllers, we have defined 4 referencetrajectories corresponding to each LSP, i.e.

qiref, 4,,1L=i as follows

++=j

ref Tjtcbatq )))12((tanh()( 3,13,13,13,1

and

)tanh(

)))12((tanh()(

6,46,4

6,46,46,46,4

tcb

Tjtcbatqj

ref

+

+=

with Tj, .

The parameters a, b determine the quantity ofdata to be transferred during (2j+1)Tperiod, and c is

used to adjust the transition between these quantities.

The parameter values are chosen to match with the

input traffic demands and as the same time to

guarantee the service performance. Mainly, to reduce

delay variation and packet loss.

The simulation results are obtained with parameter

values summarized in Table 1, for a simulation time

of120sec and packet size of1032bytes.

Table 1: Simulation parameters

q1ref q2ref q3ref q4ref Rate

a 473 350 235 235

b -73 100 -35 -35

c -0.5 0.6 -0.5 -0.5

T 22

15Mbps

for LSR 22030Mbps

for LSR 221

These values are chosen in order to bound the

queuing delay to 200ms. Also, we chose thehyperbolic tangent function because we think it

represents quite ON-OFF traffic.

The reference trajectories are shown in Fig.4. We

depict four phases of buffer planning defined byparameter T. For example, in [0, 22] sec interval,200packets can be stored for LSP4,6, 550packets for

LSP3 and 550packets for LSP1. During [22, 42] sec,

270packets for LSP4, 200packets for LSP6,

550packets forLSP1 and 350packets forLSP3, and so

on.

3.2 Simulation ResultsThe trajectory tracking control ensures the

tracking of the reference flat output qiref (figures

Fig.4, Fig.5). For example, for t [0, 22] sec, the

queue lengths of LSP4 and LSP6 are about

160packets compared to the reserved buffers for


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these paths which are about 200packets.

In other words, when transition occurs, the flatnesscontrollers increase or reduce the LERs output rates

(figure Fig.6) in smooth manner, without affecting

the transmission service as shown in figures Fig.7,

Fig.8. The queuing delays at the LSPs queues qi are

bounded by 190ms, as well as the loss ratio by (


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Figure 8: Loss variation at the LSPs buffers

Figure 9: LSPs bandwidth utilization ratio

The flatness control mechanism successfully

prevents the router's buffer oscillation and the

queuing delay variation by tracking the reserved

buffer size. Our approach is very efficient formanaging network resources as it maximizes the

buffer utilization (see figure Fig.9), in comparison to

the reserved buffers.

6. CONCLUSION

The new traffic engineering approach presented

here, aims at optimizing the network utilization and

performance by intelligently handling the buffer

reservation at the routers. We take benefit of traffic

descriptors to model communication behaviour and

QoS requirement by using trajectories. We show thatthe reactive control and the dynamic regulation using

purely control theoretic approaches stabilize the

network and avoid undesirable oscillations for

critical flow transmissions.

Unlike token bucket approach used in DiffServ for

traffic conditioning, our feedback mechanismadvertises, depending on network variation, the

sources when they exceed their profile and regulate

their input rate in order to match their QoS

requirements. As a result, a dynamic adaptation is

provided between clients and their service provider.

This controller is implemented as a new Active

Queue Management mechanism based on the

trajectory tracking method advocated here. This new

AQM approach provides guarantees for critical

traffic and may be used for dynamic provisioning of

transmission service. Obtained results illustrate thefeasibility of the flatness control law as well as the

efficiency of the proposed trajectory tracking

approach in controlling and adapting the critical

application bit rate depending on the reserved

resources and negotiated transmission constraints

namely delay requirement.

5. REFERENCES

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[20] http://www-x.antd.nist.gov/glass.

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