End-to-End Performance and Fairness in Multihop Wireless Backhaul Networks V. Gambiroza, B. Sadeghi,...
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Transcript of End-to-End Performance and Fairness in Multihop Wireless Backhaul Networks V. Gambiroza, B. Sadeghi,...
End-to-End Performance and Fairness in Multihop Wireless
Backhaul Networks
V. Gambiroza, B. Sadeghi, and E. Knightly
Rice University
Violeta Gambiroza
Backhaul Networks
InternetBackhaul network
Residential useror small business
• Backhaul networks technologies – Wireline: coax-, copper-based, fiber– Wireless
Violeta Gambiroza
Wireless Backhaul Networks TAP Networks
• Multihop wireless infrastructure– High bandwidth, good economics, deployability
• Transit Access Point (TAP)
Residential user
or small business
Ethernet
Ethernet
Ethernet
Ethernet
Wireless Backhaul
Network
Internet
Violeta Gambiroza
Fundamental Scenario
Ethernet
Ethernet
Ethernet
Ethernet
One branch of the access tree Ethernet
Ethernet
Ethernet
Ethernet
Internet
TAP1 TAP2 TAP3 TAP4
• Traffic matrix– Traffic to and from
Internet
Violeta Gambiroza
Parking Lot Scenario
• Similar to parking lot with one exit
Ethernet
Ethernet
Ethernet
Ethernet
Internet
TAP1 TAP2 TAP3 TAP4
Violeta Gambiroza
Fairness Problem
Violeta Gambiroza
Fairness Problem
Violeta Gambiroza
Fairness Problem
Goal Ensure equal shares independent of spatial location
We need multihop fairness
Violeta Gambiroza
Contributions
• Fairness reference model
• Performance study– TCP – Inter-TAP fairness algorithm
• Capacity and fairness
Ethernet
Ethernet
Ethernet
Ethernet
Wireless Backhaul
Network
Violeta Gambiroza
Outline
• Fairness reference model– Limitations of existing models– Fairness objectives– Algorithm solution space
• Performance study
• Capacity and fairness
Ethernet
Ethernet
Ethernet
Ethernet
Wireless Backhaul
Network
Violeta Gambiroza
Limitations of Existing Fairness Models: Ingress-Egress Flow Granularity
• Fairness with Ingress-Egress (IE) flow granularity– Provide fair share to each ingress-egress pair
• Ingress Aggregate (IA) flow granularity–Provide fairness on both IA and IE flow granularities -Fundamentally differentFundamentally different
• Node corresponds to TAP– TAP is small business/residence
Provide fair shares to TAPs independent of number of flows
Treat TAP’s traffic as a single aggregate
Ingress-Egress flow granularity Ingress Aggregate
flow granularity
Ingress-Egress flow granularity
Violeta Gambiroza
Our Objectives (Our Objectives vs. Classical Objectives)
Flow granularity– Ingress aggregate (IA) and Ingress-
Egress
Our Objectives Classical Objectives
– Ingress-Egress (IE)
– Bandwidth
– Wired link
Depends on fairness model
Spatial properties– Provide fair shares independent of spatial location
– Maximize spatial reuse – flows sufficiently spatially separated can transmit simultaneously
Resource– Channel access time
Medium – Multirate shared wireless
channel
Formal definition in paper
Violeta Gambiroza
Problem Statement
• Fairness reference model defined
• Distributed algorithm – Targeted at achieving shares defined by reference model
• Solution space
– Local solution – insufficient
Example: Parking lot
– Multihop solution
Flow e2e – TCP
Multihop wireless network e2e – Inter-TAP Fairness Algorithm (IFA)
Violeta Gambiroza
Outline
• Fairness reference model
• Performance study– Performance factors– TCP fairness– Inter-TAP Fairness Algorithm (IFA)
• Capacity and fairness
Ethernet
Ethernet
Ethernet
Ethernet
Wireless Backhaul
Network
Violeta Gambiroza
Performance Factors (1/2)
Factors investigated• Fairness algorithms
– Uncontrolled UDP, TCP, IFA • Media access control
– 802.11 with two-way and four-way handshake • Antenna technologies
– Omni directional, sector• Carrier sense range, multiple topologies and flow scenarios…
Other simulation specs• Channel rate constant 2 Mb/sec• 1000 byte packets
Goal
• Study end-to-end performance and fairness
Violeta Gambiroza
Performance Factors (2/2)
Well understood topologies
Increased no. of hops from destination
Reduced throughput
Increased no. of source-dest. pairs
Reduced throughput
Topology
Violeta Gambiroza
Performance Factors (2/2)
Parking lot
MU-TAP and TAP-TAP transmissions on orthogonal channels
Ethernet
Ethernet
Ethernet
Ethernet
Internet
TAP1 TAP2 TAP3 TAP4
TA(1)TA(2)
TA(3)
Topology
Violeta Gambiroza
Fairness with TCP MAC, Hidden Terminals and Information Asymmetry
320.5 320.5 320.5
1000
38.5
0
400
800
1200
1600
TA(1) TA(2) TA(3) TA(4) Total
Goo
dp
ut
[kb
/sec
]
Obj. Basic RTS/ CTS
• Idealized objective– Assumes perfect collision-
free MAC
ACK Traffic
MUs generate long lived TCP-Sack flows
Carrier sense range = transmission range Ethernet
Ethernet
Ethernet
Ethernet
TAP1 TAP2 TAP3 TAP4
Violeta Gambiroza
Fairness with TCP MAC, and Hidden Terminals and Information Asymmetry
320.5 320.5 320.5
2 20
1247
1000
38.5 48
1177
0
400
800
1200
1600
TA(1) TA(2) TA(3) TA(4) Total
Goo
dp
ut
[kb
/sec
]
Obj. Basic RTS/ CTS
ACK Traffic
MUs generate long lived TCP-Sack flows
Carrier sense range = transmission range Ethernet
Ethernet
Ethernet
Ethernet
TAP1 TAP2 TAP3 TAP4
• TAP1 and TAP2 traffic starved– Both are hidden terminals– Timeouts – significant
throughput penalty TCP generates bursts
of packets
Violeta Gambiroza
Fairness with TCP MAC, and Hidden Terminals and Information Asymmetry
320.5 320.5 320.5
2 20
1247
3 27 40.7
1000
38.5 48
1177
1058.7988
0
400
800
1200
1600
TA(1) TA(2) TA(3) TA(4) Total
Goo
dp
ut
[kb
/sec
]
Obj. Basic RTS/ CTS
• RTS/CTS exchange introduces information asymmetry [KSSK02]– TAP1 has no information
of TAP3-TAP4 trans.ACK Traffic
MUs generate long lived TCP-Sack flows
Carrier sense range = transmission range Ethernet
Ethernet
Ethernet
Ethernet
TAP1 TAP2 TAP3 TAP4
• Capacity and fairness need to be considered jointly– Total is up to 125% of
objective while two flows are starved
Violeta Gambiroza
TCP and Sector Antennas
MUs generate long lived TCP-Sack flows
TAPs use sector antennas Ethernet
Ethernet
Ethernet
Ethernet
TAP1 TAP2 TAP3 TAP4
641 641 641
2000
247313
4777
356
1386.3
730
53.3
1219
692
167
0
400
800
1200
1600
2000
2400
TA(1) TA(2) TA(3) TA(4) Total
Goo
dp
ut
[kb
/sec
]
Obj. Basic RTS/ CTS
ACK Traffic
• Impact of hidden terminals and information asymmetry mitigated
• Severe spatial bias – TAP1 traffic obtains 26% of
objective • Total goodput increased • Total goodput is 67% of the
objective
Violeta Gambiroza
Inter-TAP Fairness Algorithm (IFA)
• Idealized version of algorithm– Omniscient calculation of fair rates
Practical algorithm needs messaging
• Limit traffic rate at ingress
Violeta Gambiroza
TCP and IFA
MUs generate long lived TCP-Sack flows
Carrier sense range = transmission range
Ethernet
Ethernet
Ethernet
Ethernet
TAP1 TAP2 TAP3 TAP4
• End-to-end performance considerably improved– TAP-aggregated throughput is 59%
to 75% of the objective
• Hidden terminal problem mitigated– Contention considerably decreased– TCP cannot inject bursts of packets
320.5 320.5 320.5
1000
190 223
679
38.5 26
240
0
400
800
1200
TA(1) TA(2) TA(3) TA(4) Total
Goo
dput
[kb
/sec
]
Obj. Basic 802.11/ I FA
ACK Traffic
• Spatial bias– IFA alone cannot eliminate it
• Rates lower than the objective
Violeta Gambiroza
Inter-TAP Performance Isolation
Provide inter-TAP performance isolation independent of traffic types
ACK Traffic
326.8 326.8 326.8
993.4
209246
709.4
13 8.4
246
0
200
400
600
800
1000
TA(1) TA(2) TA(3) TA(4) Total
Goo
dput
[kb
/sec
]
Obj. Basic 802.11• TCP achieves 64% of idealized
objective, while UDP obtains 75%
• Even with balanced contention TCP reduces its rate– Having more MUs per TAP TCP
performance degraded
Each TAP has one MUTAP1: MU transmits TCP trafficTAP2 and TAP3: MU transmits UDP traffic
Ethernet
Ethernet
Ethernet
Ethernet
TAP1 TAP2 TAP3 TAP4
Violeta Gambiroza
Summary of Findings (1/2)
• Starvation of upstream flows (UDP, TCP, with or w/o RTS/CTS)– “Parking Lot” scenario results in hidden terminals and
information asymmetry
• Sector antennas and carrier sense range mitigate the hidden terminal problem– Severe spatial bias
SA: Throughput as low as 26% of targeted values CSR: Throughput as low as 34% of targeted values
• TCP able to exploit spatial reuse
Violeta Gambiroza
Summary of Findings (2/2)
• IFA approximates reference model performance
• The impact of hidden terminal problem and information asymmetry mitigated– Without any modifications to CSMA/CA
• TCP over IFA achieves 59% to 75% of idealized objective– Without any modifications to TCP
• Inter-TAP performance isolation
Violeta Gambiroza
Outline
• Fairness reference model
• Performance study
• Capacity and fairness– Maximum throughput without fairness– Fairness objectives and throughput
Ethernet
Ethernet
Ethernet
Ethernet
Wireless Backhaul
Network
Violeta Gambiroza
Problem Statement
• Compute maximum aggregate throughput– No fairness constraint
• System model– One transmission possible at time– Perfect collision-free MAC
Single contention neighborhood
Violeta Gambiroza
Aggregate Throughputwith and without Fairness Constraints
1..max11}{
F
f rl
fl
F
f
f
tf
fl
tts
Assign time-shares to maximize network throughput
Fairness constraintsFairness constraintsTemporal fairness constraint
Spatial bias removal constraint
Ingress aggregate constraint
F
f rl
fl
f
t1
1
jfji
fi CtCt frji ,
No spare time-capacitySolution:
Violeta Gambiroza
Conclusions
Fairness• Fairness reference model formally defined• Designed for multihop wireless networks
Performance study• Starvation of upstream flows• Sector antennas, larger carrier sense range, IFA
mitigate the problem• IFA approximates performance of reference model
Capacity and fairness• Need to be considered jointly
End-to-End Performance and Fairness in Multihop Wireless
Backhaul Networks
V. Gambiroza, B. Sadeghi, and E. Knightly
Rice University