Download Measurement-based models enable predictable wireless behavior

Measurement-based models enable
predictable wireless behavior
Ratul Mahajan
Microsoft Research
Collaborators: Yi Li, Lili Qiu, Charles Reis, Maya Rodrig,
Eric Rozner, David Wetherall, John Zahorjan, Yin Zhang,
Wireless Mesh Networks
Can enable ubiquitous and cheap broadband access
Witnessing significant research and deployment
But early performance reports are disappointing
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Wireless performance is unpredictable
Even basic questions are hard to answer
Wireless
Wired
How much traffic can be supported?
What if a node fails?
Optimize for a given objective
Arguably the most frustrating aspect of wireless
• Mysteriously inconsistent performance
• Makes it almost impossible to plan and manage
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An example of performance weirdness
Source
UDP throughput (Kbps)
UDP throughput (Kbps)
Source
Good
Testbed
bad-good
good-bad
Relay
Good
Sink
Bad
Relay
Sink
bad-good
UDP throughput (Kbps)
Bad
Simulation
bad-good
good-bad
2x
good-bad
Source rate (Kbps) Loss rate on the bad link
Loss rate on the bad link
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Predictable performance optimization
Given a (multi-hop) wireless network:
1. Can its performance for a given traffic pattern be
predicted?
2. Can it be systematically optimized per a desired
objective such as fairness or throughput?
Yes, and Yes, at least in the context of WiFi
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Predictability needs models
To predict if specific nodes interfere and what
happens when a set of nodes send together
R1
R2
Success of failure?
S1
S2
Without models, we must measure each
possibility separately
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Traditional wireless models
S1
S2
Typical assumptions
• Transmission range is circular
• Interference range is twice the transmission range
Then predict the result of various sending configurations
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Shortcomings of traditional models
RF propagation is a very complex, esp. indoors
• The assumptions almost always do not hold in practice
Great for asymptotic behavior characterization
• E.g., expected max throughput as a function of
number of nodes
Pretty much useless for predicting behavior in a
specific wireless network
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A move towards experimentation
Instead of relying on models, test performance
of new protocols on testbeds
Hard to say if results generalize
The lack of predictability remains
• Unless all possible configurations are tested
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Measurement-based models
Capture the “RF profile” of
the network by measuring
simple configurations
Use modeling to predict
the behavior under more
complex configurations
Can offer the best of traditional modeling and
experimentation worlds
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Lessons learned
Simple measurements on off-the-shelf hardware can
provide usable RF profile [SIGCOMM2006]
It is possible to model interference, MAC, and traffic in a
way that balances fidelity and tractability [MobiCom2007]
Holistically controlling source rates is key to achieving
desired outcomes [HotNets2007, SIGCOMM2008]
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Measurement-based modeling
and optimization
Measure the RF profile of
the network
Constraints on sending rate
and loss rate of each link
Find compliant source rates
that meet the objective
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Measurements
Measure the RF profile
of the network
One or two nodes broadcast at
a time
– O(n2) measurements
Constraints on sending rate
and loss rate of each link
Other nodes listen and log
received packets
R
S1
Find compliant source rates
that meet the objective
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S2
Yields information on loss and
carrier sense probabilities
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Modeling
Measure the RF profile of
the network
Makes no assumptions about
topology, traffic, or MAC
Lightweight yet realistic
Constraints on sending rate
and loss rate of each link
Find compliant source rates
that meet the objective
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O(# active links) constraints capture
the feasible operating region
1. Throughput constraints
2. Loss rate constraints
3. Sending rate constraints
14
Throughput constraints
Divide time into variable-length slot (VLS)
3 types of slots: idle, transmission, deferral
Expected payload
transmission time
Probability of starting
transmission in a slot
Success probability
EPi   i  (1  pi )
gi 
(1   j )  Tslot   iTi   Dij jT j
j i
j
Expected slot duration
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Loss rate constraints
Inherent and collision loss are independent
Inherent loss is directly measured
Collision loss
Synchronous loss
• Two senders can carrier sense each other
• Occur when two transmissions start at the same time
Asynchronous loss
• At least one sender cannot carrier sense the other
• Occur when two transmissions overlap
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Sending rate feasibility constraints
DIFS
Random
Backoff
Data Transmission
SIFS
ACK
Transmission
802.11 unicast
– Random backoff interval uniformly chosen [0,CW]
– CW doubles after a failed transmission until CWmax, and
restores to CWmin after a successful transmission
1
0 i 
1  CW ( pi ) / 2
Expected contention window size under loss rate pi
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Extensions to the basic model
RTS/CTS
– Add RTS and CTS delay to VLS duration
– Add RTS and CTS related loss to loss rate constraints
Multi-hop traffic demands
– Link load  routing matrix  e2e demand
– Routing matrix gives the fraction of each e2e demand that
traverses each link
TCP traffic
– Update the routing matrix: RTCP  Rdata    Rack
where  reflects the size & frequency of TCP ACKs
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Optimization
Measure the RF profile of
the network
Constraints on sending rate
and loss rate of each link
Inputs:
• Traffic matrix
• Routing matrix
• Optimization objective
– Total throughput, fairness, …
Output:
• Per-flow source rate
Find compliant source rates
that meet the objective
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Predictable: output rates are
actually achievable
19
Flow throughput feasibility testing
Input: throughput
Output:
feasible/
infeasible
Initialize τ= 0 and p = pinherent
Estimate τ from throughput and p
Estimate p from throughput andτ
no
Converged?
yes
Check
feasibility
constraints
Building block for optimization
Uses an iterative procedure
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Fair rate allocation
Initialization: add all demands to unsatSet
Scale up all demands in unsatSet
until some demand is saturated or scale1
yes
if (scale 1)
no
Move saturated demands from unsatSet to X
If unsatSet≠
yes
no
Output X
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Total throughput maximization
Formulate a non-linear optimization problem (NLP)
Solve NLP using iterative linear programming
max
x
Maximize total txput
d
d
EPi   i  (1  pi )
Link load is bounded by
s.t.  Rid xd 
(1   j )Tslot   Dij jT j throughput constraints
d
j
0 i 
1
1  CW ( pi ) / 2
0  xd  x d
*
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j
Sending rate is feasible
E2e throughput is
bounded by demand
22
10
9
8
7
6
5
4
3
2
1
0
0 1 2 3 4 5 6 7 8 9 10
Estimated throughput (Mbps)
Actual throughput (Mbps)
Actual throughput (Mbps)
The network is capable of achieving its
model-predicted throughput
8
7
6
5
4
3
2
1
0
0 1 2 3 4 5 6 7 8
Estimated throughput (Mbps)
UDP
TCP
Results for a 19-node testbed
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1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
scale=1.0
scale=1.1
scale=1.2
scale=1.5
0
0.2
0.4
Fractions of runs
Fractions of runs
The network cannot achieve higher
than model-predicted throughput
0.6
0.8
1
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
scale=1.0
scale=1.1
scale=1.2
scale=1.5
0
0.2
0.4
0.6
0.8
1
Ratios between actual and estimated throughput
Ratios between actual and estimated throughp
UDP
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TCP
24
1
1
0.8
0.8
Jain fairness index
Jain fairness index
Measurement-based models enable fair
throughput distribution (predictably)
Model based opt.
0.6
Default
0.4
0.2
0
Model based opt.
0.6
Default
0.4
0.2
0
0
4
8
Number of flows
UDP
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16
0
4
8
12
16
Number of flows
TCP
25
Measurement-based models boost
network throughput (predictably)
6
Model based opt.
5
Throughput (Mbps)
Throughput (Mbps)
6
Default
4
3
2
1
Model based opt.
5
Default
4
3
2
1
0
0
0
4
8
Number of flows
UDP
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16
0
4
8
12
16
Number of flows
TCP
26
Future work: Making it real
Online measurement of RF profile
Decentralized computation of source rates
Joint optimization of routing and source rates
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Conclusions
Wireless behavior is unpredictable
• Complex RF propagation
• Interactions between MAC, traffic, and interference
Measurement-based models: a new approach to obtain
predictable behavior
• Measure the RF profile and model the rest
Promising results in our experiments on real test beds
• Enables predictable optimization
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