conditionally guaranteed budgets for high-quality video ... · tu/e informatica, system...

Reinder J. Bril, [email protected]

TU/e Informatica, System Architecture and Networking

1

Conditionally Guaranteed Budgetsfor High-Quality Video Processing

Reinder J. Bril

28-09-2005



2

Overview

• Context

• Problem description

• Conditionally Guaranteed Budgets

• Acknowledgement and references



3

Overview

• Context

– Multimedia Consumer Terminals

– Co-operative QoS approach

– QoS control strategies (reminder)

• Problem Description





4

Overview

• Context


– Load characteristics

– Resource allocation conflict





5

Overview

• Context



– Extension of QoS approach

– Analysis




6

Overview

• Context

– Multimedia Consumer Terminals

– Co-operative QoS approach

– QoS control strategies (reminder)






7

Multimedia Consumer Terminals

DVD CDxfront end

YCinterface

IEEE 1394interface

DVB Tuner

Cablemodem

CVBSinterface

VGA

RF Tuner

Focus:

Receivers in broad-castenvironments

High-qualityvideoapplications



8

Co-operative QoS approach: V-QoS

• Aim:

– Cost-effective high-quality video processing in

software for multimedia consumer terminals

• Motivation:

– openness & flexibility

• Boundary condition:

– Cost-effectiveness

– Preserve existing system qualities

• Co-operative approach:

– Organizational

– System



9

Co-operative QoS approach: organization

Quality of Service Resource Management

(QoS-RM)

ScalableVideo Algorithms

(SVA)

V-QoS

University of Madrid (dit/UPM)

University of Illinois at Urbana-

Champaign (UIUC)

University of Mannheim

University of St. Petersburg

ITEA/Europa, ITEA/Robocop, OZONE, …



10

Co-operative QoS approach: system

Adaptive applications

Provide quality levels +

estimated resource req.

Resource managerProvides guaranteed

resource budgets

Local quality control

SVAs

…

Global system utility control

Optimizes system utility,

sets quality levels +

allocates resources

Control Hierarchy



11

QoS control strategies


Provide quality levels +

estimated resource req.

Resource managerProvides guaranteed

resource budgets

Local quality control

SVAs

…


Optimizes system utility,

sets quality levels +

allocates resources

Control Hierarchy



12

Assumptions

– Single scalable high-quality video application

– Asynchronous, i.e. works ahead to even out load

– Deadlines on the completion times of frames

– Temporal and structural load changes

– Fixed budget, lower than worst-case load

5

10

15

20

25

30

35

40

1 100 200 300 400 500 600

proc

essi

ng t

ime

(ms)

frame number

worst-case load ?

How can we make the best of it?



13

High quality video

• QoS parameters:

– deadline misses

– picture quality

– quality fluctuations

• QoS measure:

– Based on weighted sum of QoS parameters

– Reflects the user-perceived quality

deadline misses

pic

ture

quality

quality flu

ctuations



14

9.3

9.8

-55.7

2.9

9.4

29.828.3 35.429.5 34.0

-2

0

2

4

6

8

10

26 28 30 32 34 36 38 40

aver

age

reve

nue

budget (ms)

OPT

RL*MDP*

MDP

Q3

(by courtesy of Clemens C. Wüst)

Simulation results



15

Overview

• Context


– Load characteristics

– Resource allocation conflict





16

Load characteristics

• Stream dependent

• Not known a priori



17

Average revenues of streams

-2

-1

0

1

2

3

4

5

6

7

8

9

10

20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40

aver

age

reve

nue

per

proc

esse

d fr

ame

budget (ms)

concatalloallo1alloallo2alloallo3

amsterdamnedantz

deliftfallingdown

floddermuppetsnightlife

passenger57pisapsv

somewhereviezeman

violentcityworldisnotenough

yesminister

(by courtesy of Clemens C. Wüst)



18

Resource requirements

time

load

structural load

running average

temporal load



19

Resource allocation: “worst-case”

time

load

structural load

running average

temporal load

“wasted”

Not cost-effective



20

Resource allocation: close to average

time

load

Instantaneous

budget increase

Not yet feasible

structural load

running average

temporal load



21

Resource allocation: close to average

time

load

Instable output quality

Not acceptable for

important applications

structural load

running average

temporal load



22

Resource allocation conflict

Structural load increase

+

close-to-average resource allocation

yields

– either instable output quality

not acceptable for important applications

– or “wasted” resources

not cost-effective



23

Overview

• Context



– Extension of QoS approach

– Analysis




24

Conditionally guaranteed budgets: Why?

time

load

Instantaneous

budget increase

B

structural load

running average

temporal load

B

Anticipated increase



25

Conditionally guaranteed budgets: How?

Basic approach (MIA and LIA):

– Two modes of quality settings + allocation:

Normal mode

(low load for MIA)

Anticipated mode

(high load for MIA)

MIA QMIA

, BMIA

+ BMIA

QMIA

, BMIA

QLIA,N

, BLIA

+ BLIA

QLIA,A

, BLIA

LIA

CGB



26


Examples:

In an MCT,

• MIA: “disk” application, and

• LIA: “main” application.

(or dual-screen TV, where MIA has user-focus).

In a wireless environment,

• LIA: a regular application, and

• MIA: “virtual interfering application” (e.g. a

microwave).



27



Resource manager (RM)


MIA LIA

Inform MIA, LIA, and RM

about both modes

Normal

mode

Anticipated

mode

Normal

mode

Anticipated

mode

Normal

mode

Anticipated

mode

modes



28





MIANormal

mode

Anticipated

mode

Normal

mode

Anticipated

mode

LIANormal

mode

Anticipated

mode

Running in normal mode



29





MIANormal

mode

Anticipated

mode

Normal

mode

Anticipated

mode

LIANormal

mode

Anticipated

mode

Claim BMIA

MIA detects load increase,

claims BMIA

, and

switches mode

Mode transition



30





MIANormal

mode

Anticipated

mode

Normal

mode

Anticipated

mode

LIANormal

mode

Anticipated

mode

Inform LIA

RM switches mode

instantaneously, and

informs LIA

Mode transition



31




MIANormal

mode

Anticipated

mode

LIANormal

mode

Anticipated

mode

LIA switches mode


Normal

mode

Anticipated

mode

Running in anticipated mode



32


• Summary basic approach:

– Assumption:

• Anticipation of resource needs and modes;

– Allocation phase:

• Informing MIA, LIA, and RM about modes;

• Delegation of mode changes to MIA;

– Execution phase:

• Release and claim of resources by MIA;

• Instantaneous mode change by RM.



33


• How to change budgets instantaneously ?

• In-the-place-of resource consumption

– LIA consumes BLIA

exactly when

MIA would have consumed BMIA



34

In-the-place-of budget consumption

BMIA B

MIA

MIA

LIA

Anticipated mode



35


BMIA B

MIA

MIA

LIA

Normal mode



36


BMIA B

MIA

MIA

LIA

Normal mode



37


BMIA B

MIAClaim B

MIA

MIA

LIA

Mode switch



38

Conditionally guaranteed budgets

• Analysis

• How to determine BLIA

?

– Worst-case (i.e. minimal) amount that can be

guaranteed on a periodic basis.

• Cognac-glass algorithm



39

Cognac-glass algorithm

• How to determine the worst-case BLIA

?

BMIA B

MIA

MIA

LIA

R

TMIA

TLIA



40


• How to determine the worst-case BLIA

?

• Worst-case for R

when BMIA

is available:

– as early as possible for first overlapping interval;

• “best-case” analysis

– as late as possible for last overlapping interval.

• “worst-case” analysis

– based on notion of advancement

• Minimize for all values of R.



41


• Example

– fixed-priority preemptive scheduling

– set of four applications

?

MIA

LIA

A2

A1

29

31

14

6

9

-

2

1

7

period budget

Bi BiTi



42

Advancement

5 10 15 20 25 30

5

10

15

timet

BMIA

BMIA

+ BMIA

MIA

AMIA

(t)



43

Advancement

5 10 15 20 25 30

5

10

15

timet

BMIA

BMIA

+ BMIA

Worst-case advancement

WAMIA

(t)

WSMIA



44

Advancement

5 10 15 20 25 30

5

10

15

timet

BMIA

BMIA

+ BMIA

Worst-case and best-case advancement

WAMIA

(t)BAMIA

(t)



45

Advancement

• Worst-case advancement:

– WAMIA

(t) = max{ C | WRMIA

(C) t } if t > WOMIA

(0)

= 0 if t WOMIA

(0)

– with WRMIA

the worst-case response time of MIA.

– and WOMIA

the worst-case occupied time of MIA.

• Best-case advancement:

– BAMIA

(t) = max{ C | BRMIA

(C) t } if t > 0

= 0 if t 0

– with BRMIA

the best-case response time of MIA.



46

Response times and occupied times

worst-

case

best-

case

occupied timeresponse time

j

i

j ji WC

WTx

WCx1

1

j

i

j ji WC

WTx

WCx1

1

1

j

i

j ji CB

BTx

BCx1

1

1 j

i

j ji BC

BTx

BCx1

1

where:

- worst-case: smallest value satisfying the equation

- best-case: largest value satisfying the equation



47

Situation of worst-case availability

5 15 20 25 30

5

10

15

timet

35 40 45 50 55 6010

TMIA

TMIA

BMIA

BMIA

+ BMIA

BAMIA(t) WAMIA(t TMIA)



48


5 15 20 25 30

5

10

15

timet

35 40 45 50 55 6010

TLIA

BMIA

BMIA

+ BMIA

R

BBMIA( R)

BWMIA( R+TLIA TMIA)

BBMIA

(t) = max{0, min{ BMIA

, BMIA

+ BMIA

– BAMIA

(t)}}

BWMIA


, WAMIA

(t) – BMIA

}}

BLIA

=R

min{ BBMIA

(R) + BW

MIA(

R+ T

LIA– T

MIA)}



49


5

5 15 20 25 30 timet

35 40 45 50 55 6010

TLIA

BMIA

R

BBMIA( R)

BWMIA( R+TLIA TMIA)

BLIA

=R

min{ BBMIA

(R) + BW

MIA(

R+ T

LIA– T

MIA)}

BBMIA


, BMIA

+ BMIA

– BAMIA

(t)}}

BWMIA


, WAMIA

(t) – BMIA

}}



50


5

5 15 20 25 30 timet

35 40 45 50 55 6010

TLIA

BMIA

R

BBMIA( R) BW

MIA( R+TLIA-TMIA)



51


5 15 20 25 30 timet

35 40 45 50 55 6010

5

TLIA

BMIA

R

The curve looks like the shape of a glass.

Changing the relative phasing R

is like tilting the glass…

hence, cognac-glass algorithm.



52

Worst-case BLIA

as a function of TLIA

5 15 20 25 30

5

TLIA

BMIA

35 40 45 50 55 6010

BLIA

TLIAEU

+TMIATLIAEU

MIA

MIALIA

avgLIA T

BTB

avgLIAB



53


• Summary of the analysis:

– Based on notion of advancement;

– requires best-case next to worst-case analysis;

– (can be restricted to subset of values for R).

• See [Bril 04] for:

– a generalization to arbitrary periods TMIA

and TLIA

;

– formalization;

– efficient calculation.



54

Overview

• Context

• Problem description





55

Acknowledgement

• Research:

– Emile H.J. Aarts;

– Wim F.J. Verhaegh;

– Peter D.V. v.d. Stok;

– Gerhard Fohler;

– TU/e, SAN group members.

• V-QoS program:

– Christian Hentschel;

– Clara M. Otero Pérez;

– Clemens C. W st;

– Other program members and partners.



56

References

[Wüst et al 04] C.C. Wüst et al, QoS ControlStrategies for High-Quality Video Processing,

Real-Time Systems, 30(1-2): 7 – 29, May

2005.

[Bril 04] R.J. Bril, Real-time scheduling for media processing using conditionally guaranteed budgets, PhD thesis TU/e, IPA

Dissertation Series 2004 – 13, Sept. 2004,

http://alexandria.tue.nl/extra2/200412419.pdf.

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