Fair Real-time Traffic Scheduling

over A Wireless Local Area Network

Maria Adamou, Sanjeev Khanna,

Insup Lee, Insik Shin, and Shiyu Zhou

Dept. of Computer & Information Science

University of Pennsylvania, USA

2

Real-time Communication over Wireless LAN

BSMH1

MH3

MH2

3

Wireless LAN MAC Protocol IEEE 802.11 – standard

DCF (distributed) Contention-based transmission

PCF (centralized)

Contention-free (CF) transmission BS schedules CF transmissions by polling

4

Wireless Network Characteristics Unpredictable Channel Error

location dependent bursty

BSMH1

MH3MH2

5

Challenges How do channel errors affect real-time

transmissions? QoS degradation Wireless channel error model

How does BS schedule real-time transmissions with unpredictable errors? Real-time scheduling objective considering

QoS degradation with errors Real-time scheduling algorithm

6

Outlines Real-time traffic model Scheduling objectives Theoretical results Online scheduling algorithms Simulation results Conclusion

7

Real-time Traffic Model Periodic packet generation (release time) Soft deadline

Upon missing deadline, a packet is dropped

Acceptable packet loss (deadline miss) rate Degradation = actual loss rate – acceptable loss

rate

The same packet length (execution time)

8

Scheduling objectives1. Fairness (considering each flow)

Location dependent channel errors Minimizing the maximum degradation

2. Throughput (considering the system) Maximizing the overall system throughput

(fraction of packets meeting deadlines)

Online scheduling algorithm without knowledge of error in advance

9

Theoretical results No online optimal algorithm

Performance ratio of an online algorithm w.r.t. optimal for throughput maximization, two for achieving fairness, unbounded For the combined objectives, unbounded

A polynomial time offline algorithm that optimally achieves our scheduling objectives

10

Online scheduling algorithms EDF (Earliest Deadline First)

GDF (Greatest Degradation First)

EOG (EDF or GDF)

LFF (Lagging Flows First)

11

EDF (Earliest Deadline First)when a new packet is available

3 0.2

Di εi

4 0.4 3 0.3 1 0.1

EDF QueueScheduler

when it dispatches

12

GDF (Greatest Degradation First)when a new packet is available

3 0.2

Di εi

1 0.1 3 0.3 4 0.4

GDF QueueScheduler

when it dispatches

13

EOG (EDF or GDF)when a new packet is available

3 0.2

4 0.4 3 0.3 1 0.1

EDF Queue

Scheduler

when it dispatches

1 0.1 3 0.3 4 0.4

GDF Queue

If there is a packet that will miss its deadline after next slot

Otherwise

14

LFF (Lagging Flows First)when a new packet is available

3 0.2

Di εi

4 0.4

LFF Array

4

index

2

1 0.11

3 0.33

15

LFF (Lagging Flows First)when a new packet is available

3 0.2

Di εi

4 0.4

LFF ArrayScheduler

when it dispatches

4

index

2

1 0.11

3 0.33

3 0.2

16

LFF (Lagging Flows First)when a new packet is available

3 0.2

4 0.4 2 0.3 1 0.1

EDF Queue

Scheduler

when it dispatches

1 0.1 2 0.3 4 0.4

GDF Queue

If there is a packet that will miss its deadline after next slot

Otherwise

17

Simulation – Performance Metrics

1. Degradation (for each flow) Fraction of packets lost beyond the

acceptable packet loss rate

2. Throughput (over all flows) Fraction of successfully transmitted

packets

18

Simulation – Error Modeling Random blackouts (wi) for error period

Error duration rate =

BSMH1

MH3MH2

MH1

tmaxt0

MH2 MH3

wi

maxt

wi

i

19

Results – Max Degradation

0

0.1

0.2

0.3

0 0.1 0.2 0.3 0.4Error Duration Rate

Deg

rada

tion

deg

ree

EDF

GDF

EOG

LFF

20

Results – Throughput Ratio

0.98

0.985

0.99

0.995

1

1.005

1.01

1.015

1.02

0 0.1 0.2 0.3 0.4Error Duration Rate

Thr

ough

put r

atio

vs

EO

G

EDFGDFEOGLFF

21

Related Work QoS guarantees over wireless links

No consideration of fairness issue WFQ over wireless networks

No consideration of deadline constraint QoS degradation considering deadline

Imprecise computation IRIS (Increased Reward with Increased Service) (m,k)-firm deadline model DWCS (Dynamic Window-Constrained Scheduling)

22

Conclusion Scheduling objectives

1. Fairness – minimizing the maximum degradation

2. Overall throughput maximization

Theoretical results No online algorithm can be guaranteed to

achieve a bounded performance ratio for the scheduling objective

23

Conclusion Online algorithms

For fairness objective1. LFF 2. GDF 3. EOG 4.EDF

For maximum throughput objective1. EDF 2. LFF3. EOG 4.GDF

Future work Variable length packets Other measures of fairness