Fair Real-time Traffic Scheduling over A Wireless Local Area Network
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Transcript of Fair Real-time Traffic Scheduling over A Wireless Local Area Network
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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
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Real-time Communication over Wireless LAN
BSMH1
MH3
MH2
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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
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Wireless Network Characteristics Unpredictable Channel Error
location dependent bursty
BSMH1
MH3MH2
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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
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Outlines Real-time traffic model Scheduling objectives Theoretical results Online scheduling algorithms Simulation results Conclusion
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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)
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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
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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
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Online scheduling algorithms EDF (Earliest Deadline First)
GDF (Greatest Degradation First)
EOG (EDF or GDF)
LFF (Lagging Flows First)
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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
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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
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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
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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
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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
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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
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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
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Simulation – Error Modeling Random blackouts (wi) for error period
Error duration rate =
BSMH1
MH3MH2
MH1
tmaxt0
MH2 MH3
wi
maxt
wi
i
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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
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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
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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)
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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
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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