networked media lab.1 weighted backpressure scheduling in ieee 802.11 wireless mesh networks jaeyong...

Networked Media Lab. 1

Weighted Backpressure Scheduling in IEEE 802.11 Wireless Mesh Networks

Jaeyong Yoo

[email protected]

23-11-09


Background-Link / Packet Scheduling-

N1

N2 N5

N7 N6

N4Sender 1

Receiver 1

Sender 2 Receiver 2

• Packet Scheduling– Which queue should be serviced first?

• Link Scheduling– Which link should be activated first?

• Objective– Throughput optimal– Fairness optimal– Stability Achievable Rate of S1

(Red Flow)

Ach

ieva

ble

Rat

e of

S2

(Blu

e Fl

ow)

Objective 1.Throughput Optimality

Objective 2.Fairness Optimality

Objective 3.Stability


Scheduling Research MapNotation: My queue length=X, upstream queue length=Y

MWS(Maximum Weight Scheduling)

a.k.a Back-pressureScheduling

GMS(Greedy Maximal

Scheduling)

Schedule policy: X - Y

’92 TAC

‘95 UCB

Throughput efficiencyof GMS

‘09 Mobihoc

Distributed GMS

‘06 INFOCOM

Capacity Region ofGMS

‘08 INFOCOM

Interference Condition GMS

‘08 INFOCOM

Schedule policy: X(a.k.a no message passing)

Tradeoff study betweenMessage passing vs no message passing

‘08 Mobihoc

Scheduling without“frequent” message passing

‘09 TWC

Q-CSMA

Yet Published

Schedule policy: Y

EZ-flow

‘09 CoNEXT

Implemented System2009 INFOCOM: DiffQ

Implemented System2008 Mobicom: Horizon

Scheduling Policy Evolves

TimeFlies

Schedule policy: βX + γY

(WBS)Weighted

BackpressureScheduling


Motivational Argument

• Queue non-stability while applying backpressure in IEEE 802.11 wireless mesh networks

• Query: Why non-stability comes? Despite the fact that many articles say backpressure is stable!

– Previous implementation work DID NOT provide rigorous analysis on this part

– Hence, we are doing this

– Anyway, what is our major suspect?


Suspect: Implementational approximation

Network of Dream

Theory to Practical

Link-schedulingAssumption 1:

Globally synchronized slotted access

Link-scheduling Assumption 2:

Perfect link schedule(At least

fine-grained priority access)

Link-schedulingAssumption 3:

Immediate Link Schedule

Many implicit assumption(Do not agree with reality)

IEEE 802.11-based Wireless Mesh Networks

Backpressure Backpressure

Link-scheduling Assumption 2:

Quantized Priority Access

(4 levels) Link-scheduling Assumption 2:Priority queue

(Queuing delay)

Approximation arrow

Approximation arrow

Many other constraints(Even “currently unknown”)


Main Argument

• Under the following conditions, – Quantized Priority Link Access, – Scheduling Time Delay (MAC layer queuing delay), and– Heterogeneous Link Qualities,

• Backpressure (X-Y) does not provide stable network queues

• But, βX + γ Y, with β < - γ– There exists values (β, γ, β < - γ) that stabilize network queues

– Under the following conditions• Quantization Level > 2• Any scheduling time delay• “non-critical” link-quality heterogeneity, a.k.a drift


System Model

• General n-hop case– Throughput model

– Queue evolution model

– Define drift• Difference between two adjacent link’s throughput

– Configurable queue limit: C, quantization step: L

),(),( 11 iiiik NNU

Abstract factor that contains link errors and

other flow impact


Main Result of 2-hop case

• In 2-hop case– Under the drift condition of “non-critical” drift

– If beta and gamma satisfies below two conditions,the network queue becomes stable

– With converging point to

],1[,|,~~

||| njiTTU jik


Let’s validate

• Experimental Validation Method– Implement the described system– By changing beta & gamma, observe the behavior


System Implementation

madwifi

Priority queues

Bypassing (Using PF_PACKET + RAW_SOCK + IPPROTO_RAW)Kernel

Click

f1 f2 f3 f4 f5Q Q Q Q QPer

Flowtable

Sched

P P P P P

Choose Highest Schedule Priority

athhal

Antenna

• A view of a router below IP layer

Scheduling Determination

Taken

Scheduling Action Taken

Discrepancy ofScheduling time


System Implementation (cont’d)

• Effort to minimize MAC-layer queuing delay


System Implementation (cont’d)

• Notable bugs– Unordered packet delivery

• Unordered queue length monitor [fixed by filtering through ID field of IP header]

– Madwifi 5th queue problem• 5th queue has much higher access probability even if we

change cwmin


Experimentation Env.

• Experimentation time = 3 minutes• Change beta and gamma (step 0.2)

– 0 <= Beta < 2– -2 < Gamma <= 0– Total 121 points

• C = 100• L = 30• Madwifi Priority queues: 8 queues• Manipulate channel quality change by inserting random error

– Drift (Up and Down drift)


Beta, Gamma, Out of range

• Mis-configuration of Beta and Gamma

(2, -1.8)

(1.2, -1.0)

(0.4, -0.2)


Beta, Gamma, Out of range(Deep Inside)

• Inside of [0.4, -0.2], C=100

N1 RET

N2 RET

N1 MAC

N2 MAC

N2 Queue

N1 monitoredQueue


Beta, Gamma, Out of range(Deep Inside)

• Inside of [0.4, -0.2], C=100

N2 Queue

N1 monitoredQueue

QuantizedPrioritySpace


N1 RET

N2 RET

N1 MAC

N2 MAC

N2 Queue

N91monitoredQueue

Beta, Gamma, In range(Deep Inside)

• Inside of [0.6, -0.2], C=100


Beta, Gamma, In range(Deep Inside)

• Inside of [0.6, -0.2], C=100

N2 Queue

N1 monitoredQueue


Link-error impulse


Beta, Gamma, In range(Deep Inside: Drift changing)

N2 Queue

N1 monitoredQueue


• Inside of [0.6, -0.2], C=100Drift direction changing point


Overall Comparison• From Model (Dark point represents stable point)

Gamma0-2

Beta

0

2


Overall Comparison (cont’d)• Average Queue length• (Drift Down)

• Average Queue length• (Drift Up)


Overall Comparison (cont’d)• Deviation Queue length• (Drift Down)

• Deviation Queue length• (Drift Up)


Conclusion

• Very first analysis of queue stability with considering real-world constraint– Three constraints

• Delay, Drift, Quantized Priority Space

– Provides rule of thumb

• Next step– Analysis is focusing on “averaged behavior”

– What about network variance?

– Will narrow down the choice of beta and gamma

• Adaptive algorithm that finds beta and gamma


Backup slides


Scheduling Framework

Node j

<n per-flow queues>

Node j-1 Node j+1

qj,1 qj,n

q( qj,1, Qj+1,1)Qj,1

p(qj,1, Qj+1,1)Pj,1

Qj+1, 1Qj, 1

qj-1,1 qj-1,n qj+1,1 qj+1,n

qj,i means queue length of node j of i flow

Pj,i means the priority of node j of i flow


Positioning Under Scheduling Framework

†. Two functions q, p can describe various invariants of packet scheduling

1. Back-pressure scheduling[ q(x, y) = x ] [p(x, y) = x – y ]

2. Ez-Flow[ q(x,y) = x] [p(x, y) = -y]

3. PNCP[ q(x,y) = (x+y)/2] [ p(x, y) = x – y ]

4. No-message passing[ no necessary of q] [ p(x, y) = x ]

5. Our proposal[q(x,y) = x + αy] [q(x, y) = βx + γy]


Throughput Model Validation

<From Model> <From Experimentation>


Physical Behavior with Three Conditions

L

0 t

C

t

Priority

Queue len

Ctb )(

)( dtbC

Send Faster

d

d

S

R


Imagine of slotted contention status - 1

10 9 8 7 6 5 4 3 2 1

10 9 8 7 6 5 4 3 2 1 0

Delay = 6C=5

[C-1 = 5] [7-C = 2] N1 wins[C-2 = 3] [8-C = 3] Let’s say N1 wins

[C-3 = 2] [7-C = 2] Let’s say N1 wins

[C-4 = 1] [8-C = 3] Let’s say N2 wins[C-5 = 0] [7-C = 2] Let’s say N2 wins

[C-6 = -1] [6-C =1] Let’s say N2 wins[C-7 = -2] [5-C =0] Let’s say N2 wins

[C-8 = -3] [4-C =0] Let’s say N2 wins[C-7 = -2] [3-C =-2] Let’s say N1 wins

Going up at b=3


10 9 8 7 6 5 4 3 2 1

10 9 8 7 6 5 4 3 2 1 0

Delay = 6C=5γ :0.5

[C-0.5 = 4.5] [7-2.5 = 4.5] Let’s say N1 wins[C-1 = 4] [8-2.5 = 5.5] N2 wins

[C-1.5 = 3.5] [7-2.5 = 4.5] N2 wins

[C-2 = 3] [6-2.5 = 3.5] N2 wins[C-2.5 = 2.5] [5-2.5 = 2.5] Let’s say N1 wins

[C-3 = 2] [4-2.5 = 1.5] Let’s say N2 wins

Going up at b=3

Imagine of slotted contention status - 2


Relationship between delay & drift

• C-b(n-d) = b(n)-C– Increasing case

• b(n-d) = b(n) – d

• C-b(n) – d = b(n)-C

• b(n) = C – d/2

– Decreasing case• b(n-d) = b(n) + d

• C-b(n) + d = b(n)-C

• b(n) = C + d/2

Conjecture

Amplitude of oscillation will follow

Ud

networked media lab.1 weighted backpressure scheduling in ieee 802.11 wireless mesh networks jaeyong...

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