Transcript Simulation Model

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
Real-time Communication over Wireless LAN
MH1
BS
MH2
MH3
2
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
3
Wireless Network Characteristics

Unpredictable Channel Error
location dependent
 bursty

MH1
BS

MH2
MH3
4
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
5
Outlines

Real-time traffic model

Scheduling objectives

Theoretical results

Online scheduling algorithms

Simulation results

Conclusion
6
Real-time Traffic Model

Periodic packet generation (release time)

Soft deadline


Acceptable packet loss (deadline miss) rate


Upon missing deadline, a packet is dropped
Degradation = actual loss rate – acceptable loss
rate
The same packet length (execution time)
7
Scheduling objectives
1. 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
8
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
9
Online scheduling algorithms

EDF (Earliest Deadline First)

GDF (Greatest Degradation First)

EOG (EDF or GDF)

LFF (Lagging Flows First)
10
EDF (Earliest Deadline First)
when a new packet is available
3
4
0.4
Di εi
0.2
3
0.3
EDF Queue
1
0.1
when it dispatches
Scheduler
11
GDF (Greatest Degradation First)
when a new packet is available
3
1
0.1
Di εi
0.2
3
0.3
GDF Queue
4
0.4
when it dispatches
Scheduler
12
EOG (EDF or GDF)
when a new packet is available
3
4
0.4
If there is a packet that will
miss its deadline after next slot
0.2
3
0.3
1
0.1
when it dispatches
EDF Queue
1
0.1
3
0.3
GDF Queue
4
0.4
Scheduler
Otherwise
13
LFF (Lagging Flows First)
when a new packet is available
3
index Di
εi
0.2
4 4
3 3
2
1 1
0.4
0.3
0.1
LFF Array
14
LFF (Lagging Flows First)
when a new packet is available
3
index Di
εi
0.2
4
3
2
1
4
3
3
0.4
1
0.1
0.3
0.2
LFF Array
when it dispatches
Scheduler
15
LFF (Lagging Flows First)
when a new packet is available
3
4
0.4
If there is a packet that will
miss its deadline after next slot
0.2
2
0.3
1
0.1
when it dispatches
EDF Queue
1
0.1
2
0.3
GDF Queue
4
0.4
Scheduler
Otherwise
16
Simulation – Performance Metrics
1.
Degradation (for each flow)

2.
Fraction of packets lost beyond the
acceptable packet loss rate
Throughput (over all flows)

Fraction of successfully transmitted
packets
17
Simulation – Error Modeling

Random blackouts (wi) for error period

 wi
Error duration rate = i
t max
t0
wi

MH1
MH2
MH3
tmax



MH1
BS

MH2
MH3
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Results – Max Degradation
Degradation degree
0.3
EDF
GDF
EOG
LFF
0.2
0.1
0
0
0.1
0.2
0.3
Error Duration Rate
0.4
19
Results – Throughput Ratio
Throughput ratio vs EOG
1.02
EDF
GDF
EOG
LFF
1.015
1.01
1.005
1
0.995
0.99
0.985
0.98
0
0.1
0.2
0.3
Error Duration Rate
0.4
20
Related Work

QoS guarantees over wireless links


WFQ over wireless networks


No consideration of fairness issue
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)
21
Conclusion

Scheduling objectives
1.
2.

Fairness – minimizing the maximum
degradation
Overall throughput maximization
Theoretical results

No online algorithm can be guaranteed to
achieve a bounded performance ratio for the
scheduling objective
22
Conclusion

Online algorithms
 For fairness objective
1. LFF

3. EOG
4.EDF
For maximum throughput objective
1. EDF

2. GDF
2. LFF
3. EOG
4.GDF
Future work


Variable length packets
Other measures of fairness
23