Modeling and Simulation of a LFVC

Scheduler

Prof. Antonio M. AlbertiINATEL: Instituto Nacional de Telecomunicações

National Institute of Telecommunications

Santa Rita do Sapucai

Brazil

Presentation Outline

� Introduction

� Leap Forward Virtual Clock

� Developed Model

� Interaction Between LFVC and ATM Network Models

� Model Validation

� Performance Evaluation

� Final Remarks

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Introduction

� One of the most important issues in integrated services

networks is the choice of the service discipline to be used

at each packet queuing point in order to select the

appropriate packet service order.

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Why?

1. Service disciplines affect network performance not

only in terms of delay and loss, but also in terms of

throughput and fairness.

2. Service disciplines become a key to offer QoS

isolation among connections/flows in the network.

Introduction

� Amongst current service disciplines, the ones that

approximate Generalized Processor Sharing (GPS) have

had a lot of success satisfying such requirements.

� In 1993, Parekh and Gallanger demonstrated that

employing GPS servers in network switches, end-to-end

QoS guarantees can be provided for a connection.

� However, GPS is an idealized discipline that does not can

be implemented in real world.

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Introduction

� So Parekh and Gallanger proposed a packet-based

approximation to the GPS, which was called Packet-by-

Packet Generalized Processor Sharing (PGPS).

� In 1996, Bennett and Zhang developed a new algorithm to

approximate the GPS called Worst-case Fair Weighted Fair

Queuing (WF2Q).

� Bennett and Zhang have demonstrated that WF2Q can

work almost identical as GPS.

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Introduction

� Several other service disciplines have been developed

since then.

� However, according to Suri et. al., just two disciplines can

work almost identical as GPS: WF2Q and Leap Forward

Virtual Clock (LFVC).

� In addition, there are two important differences among

these algorithms:

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1. LFVC is simpler to implement than WF2Q.

2. LFVC has a smaller computational overhead .

Introduction

� These factors motivated us to implement LFVC algorithm in

the context of an ATM network model previously developed

to trustworthily evaluate QoS in ATM networks through

simulation.

� The LFVC algorithm is fundamental in this network model,

since very simple scheduling algorithms aren’t capable to

capture service differences among connections.

� Our LFVC scheduler model interacts with the other models

from this model set.

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Leap Forward Virtual Clock

� LFVC is a work-conserving fair-share scheduler.

� It will be never turned off if there are cells waiting for service.

� An ATM cell flow f which temporarily has used more

bandwidth than allocated through a weight φf can be disciplined by placing the exceeding cells in a low priority

queue L.

� However, well-behaved cell flows are stored in a high

priority queue H.

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Leap Forward Virtual Clock

� Virtual clock service disciplines work allocating tags for

each cell waiting for service.

� These tags represent the system clock value, when a cell

will be served.

� Therefore, ATM cells are served in an increasing order of

their tags.

� Just cells in the H queue are served.

� Cells in the L queue must be transferred to the H queue, in

order to be served.

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Leap Forward Virtual Clock

� So, how long can the cells can be maintained in the L

queue without the risk of an excessive delay?

� The maximum delay that cell c can suffer is:

� is the value of tag for cell c;

� is the current virtual clock value;

� is the time required for cell c be served with the rate

allocated to flow f.

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( ) fstcT ∆≥−

( )cT

st

f∆

Leap Forward Virtual Clock

� It still remained another problem: what happens if all flows

have been transferred for queue L and queue H becomes

empty?

� The solution for this problem was to advance the server

clock as far forward as possible, without violating the delay

invariant of any flows in L.

� After the leap forward step, at least one active flow in L

becomes eligible for transferring to H.

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Developed Model

� To implement the H and L queues, a priority queue data

structure was used.

� Besides priority queues H and L, the original algorithm

uses a FIFO queue for each flow f. This queue is called Qf.

� In fact, it is the queue Qf that stores the cells, while the H

and L priority queues just handle the service order and

which flow is oversubscribed or not.

� In our implementation, this per-flow queue already exists in

another model of the ATM models set: Per-VC Queuing

model. Mo

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Developed Model

� The developed model has two main algorithms:

� ReceiveCell: To receive a new cell in the LFVC scheduler.

� It has one subroutine:

• ProcessHead: to process the head of the Qf queue.

� TransmitCell: To transmit a cell to outside the scheduler.

� This algorithm has two subroutines:

• TransferCells: to transfer cells from the L queue to the H queue.

• ServiceCell: to serve a cell whose token waits in the H queue.

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Developed Model

� ReceiveCell

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ReceiveCell

( )

?1=fQ Yes

No

End

Call ProcessHead fQ

Looks for the occupation of

the queue .fQ

mt

Developed Model

� ProcessHead Subroutine

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- Flow f weight.

- FIFO queue for flow f.

- Previous tag of a flow f.

- Current scheduler timer.

fφ

ft

fQ

prev

ft

- Time period required for the

transmission of a flow f cell in

the rate allocated for this flow.

f∆

st

- Transmission frames period.τ

- Rounding parameter.ρ

- Scheduler capacity in cells/second.SC

- Arrival time of a cell from flow f.mt

- Service time.lt

Looks for the pointer of the

cell in the head of the

queue .

Recover and .prev

ft

fQ

fφ

SCf

f.

1

φ=∆

( ) f

prev

fsf ttt ∆+= ,max

f

prev

f tt =

SC

1=τ

?ρτ ++∆+≤ fsf tt Yes

No

ProcessHead .

( )fQ

mt

Schedule cell in the H

priority queue with the tag

field set up to .ft

Schedule cell in the L

priority queue with the tag

field set up to .fft ∆−A

Developed Model

� ProcessHead Subroutine

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Return

There are cells in

the H or L queues?

Yes

Schedule cell transmission

of the H queue to the time

instant . τ+mt

Turn off scheduler. No

Turn on scheduler.

Scheduler is

“turned on”?

NoSchedule cell transmission

of the H queue to the time

instant equal to the

beginning of the next

frame period. Yes

A

- Current tag of a flow f.

- Flow f weight.

- FIFO queue for flow f.

- Previous tag of a flow f.

- Current scheduler timer.

fφ

ft

fQ

prev

ft

- Time period required for the

transmission of a flow f cell in

the rate allocated for this flow.

f∆

st

- Transmission frames period.τ

- Rounding parameter.ρ

- Scheduler capacity in cells/second.SC

- Arrival time of a cell from flow f.mt

- Service time.lt

Developed Model

� TransmitCell

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TransmitCell ( )lt

End

Call TransferCells

H queue is

empty?No Call ServiceCell

Yes

Turn off the scheduler.

Developed Model

� TransferCells Subroutine

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While there are cells in L

queue.

Transfer cell to H queue.

( )( )ρ−= min,max ktt ss

IfYes

ρτ ++≤ stkmin

Yes

Return

Loop end

If

empty

TransferCells

No

H queue is

empty?

No

Developed Model

� ServiceCell Subroutine

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Return

Remove cell from head of

H queue.

τ+= ss tt

Schedule processing of

the queue head to the

instant .fQ

lt

ServiceCell

Schedule an event to the

Per-VC Queuing informing

that the cell must be

removed from the

queue .fQ

Schedule an event to carry

the served cell to the next

model at the instant .

Calculate end of service

time .τ+= lend tt

endt

Interaction Between LFVC and ATM Network Models

� LFVC model was implemented as a Scheduler (S) model

in the ATM Network Model.

� LFVC is used to define the service order of the cells stored

in Queuing Structure (QS) models, such as Per-VC

Queuing.

� The weight (φφφφf) of each flow f is calculated by a Connection Admission Control (CAC) model when a new connection is

being established.

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Interaction Between LFVC and ATM Network Models

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Broadband Terminal Equipment

ATM Adaptation Layer

QS

S

BTE ATM Layer

Input Physical

Layer

Output Physical

Layer

QS

S

CACCAC

BMBM

SDSD

TPTP

General Application

Connection

Requesting

and Deleting

Traffic

Source

Connection

Ending

Traffic

Receiver

Activate

Traffic

Source

Delete

DC

and

NCConclude

DC

and NC

To an

ATM

network

model

To an

ATM

client

model

Switch

QS

S

Switch ATM Layer

Input Physical

Layer

Output Physical

Layer

QS

S

Switch Fabric

BM

SD

QS

CAC

S

S

CACCAC

BMBM

SDSD

TPTP

TP

To other

ATM

network

model

To another ATM network model

Legend:

Packet Flow

Cell Flow

Layers

Traffic

Managers

Traffic

PolicingTP

Selective

DiscardSD

Buffer

ManagementBM

Connection

Admission

ControlCAC

SchedulerS

Queuing

StructureQS

Model Validation

� Model validation was done through service order analysis.

� There are 10 applications (1-10) transmitting exactly 1 cell

at time 0.

� For these applications, we configured a weight 0.05.

� One more application (11) transmits 10 cells starting at

time 0, with a cell interval equals to 1 second.

� This application has a weight 0.5.

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Model Validation

� Evolution of the

LFVC variables

when cells are

processed by

ProcessHead

subroutine at

the time instant

tm.

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Model Validation

� Occupation of the

FIFO queue for

flow f (Qf) in the

Per-VC Queuing

model.

� LFVC model

produced the

same service

order shown by

Suri et. al. Mo

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Performance Evaluation

�Network Topology

Modeling and Simulation of LFVC Scheduler

Performance Evaluation

� ATM Client Technologies Models Set Up

� App_0 up to App_2:

� They established connections to the App_5 using nrt-VBR service

category.

� They transmitted a MPEG-4 Simple Program Transport Stream

previously adapted to be carried over ATM networks.

� The ATM traffic contract elements are configured according with:

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Performance Evaluation

� ATM Technology Models Set Up

� BTE_0, Switch_0 and BTE_1:

� They used the following models:

• Per-VC Queuing Structures

• LFVC Schedulers

• Effective Bandwidth Allocation Algorithms

• Dynamic Partitioning Algorithms

• CLR Selective Discard Algorithms

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Performance Evaluation

� Simulations Set Up

� Three applications scenarios have been considered in

simulations:

I. Just App_0 transmits.

II. Applications App_0 and App_1 transmit.

III. App_0, App_1 and App_2 transmit.

� For each scenario we run 8 simulations. In each of them,

BTE_0, BTE_1 and Switch_0 QSs capacity were set to

16000, 8000, 4000, 2000, 1000, 500, 100 and 50 cells,

respectively.

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Performance Evaluation

� Numerical Results

� Weight φiallocated by

CAC algorithm for

connections 0, 1 and 2

considering queuing

structure capacities

ranging from 16000

cells (left) to

50 cells (right).

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Performance Evaluation

� Numerical Results

� Mean per-VC

queuing occupation

in the output queuing

structure of BTE_0.

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Performance Evaluation

� Numerical Results

� Mean cell delay

in the output queuing

structure of BTE_0.

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Performance Evaluation

� Numerical Results

� Mean cell loss

ratio in the

output physical

layer of BTE_0.

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Final Remarks

� The LFVC model interacts with other models of the ATM

model set, improving its quality.

� Numerical results validated our model, since it produced

the same service order than the original algorithm.

� Results briefly demonstrated how our model can be used

to analyze QoS in ATM networks.

� Results showed that LFVC scheduler is capable of

isolating traffic effects among ATM connections.

� Future works include a performance comparison between

LFVC scheduler and WF2Q scheduler.

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Thank You!

alberti@inatel.br