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A Perspective on Routing in A Perspective on Routing in

A Perspective on Routing in - PowerPoint Presentation

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A Perspective on Routing in - PPT Presentation

Wireless Mesh Networks A Bhorkar University Qualifying Examination University of California San Diego Motivation 2 Wireless Mesh Networks Characteristics Fixed unlimited energy virtually unlimited processing power ID: 246043

routing congestion cdp delay congestion routing delay cdp adaptor opportunistic packets performance link node reward packet diversity policy design wireless network mac

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Slide1

A Perspective on Routing in Wireless Mesh Networks

A. BhorkarUniversity Qualifying ExaminationUniversity of California, San DiegoSlide2

Motivation

2

Wireless Mesh Networks

Characteristics

:

Fixed, unlimited energy, virtually unlimited processing power

Dynamism

– Link Quality

Optimize

Low latency, High throughputSlide3

3

Research Objectives

3

To design

a routing

policy that

exhibits good

delay performance

1

Congestion Diversity Protocol (CDP)

Propose a metric that reacts

to congestion

Experimental

testbed

in Calit2

Evaluate CDP

To design an opportunistic routing policy that enhances

throughput by

minimizing the expected number of

transmissions

2Slide4

Congestion Impact

Congestion

Increase

in traffic

Increase in network congestion

Increase in buffer packet drops

and delay

4

Need

to provision for

congestion in the networkSlide5

5

Time

invariant policies

AODV

Hop count

GeRaF

Geographical

distance

SRCR

ETX

metric

Why New

Routing Policy?

S

D

Susceptible

to

congestion

Do not account for queue congestion Slide6

Selects forwarder with the

smallest queue backlogSpreads the trafficIncreases contention and

delay

6

Time variant (congestion-aware)

Multipath Routing

(SMR-LB)

Create multiple (2) paths

Timeshare routes proportional to their average delay

Roots in minimum delay routing [Gallager84]

Slow to react

Backpressure routing

(BCP 10)

Why New

Routing Policy?

S

D

Need routing policy suitable for all traffic conditionsSlide7

OutlineCDP: A bird’s eye viewCDP components

CDP issuesEvaluation Discussion and ongoing work7Slide8

Congestion Diversity Protocol : Bird’s eye view

GoalDecentralized computation of the proposed congestion measureRoute efficiently to avoid congestion

S

0.5

0.8

0.8

0.8

0.9

D

CDP’s

core is congestion

a

ware

r

outing

Routing decisions are based on congestion in the network

It exploits

p

ath

diversity

when available

8Slide9

Congestion Measure

Nodes are ordered according to a congestion measure Vi (t)V

i (

t

)

is the expected delay to reach the destination

V

i

(

t

)

<

V

j

(t

)

for all j

i

has lowest

rank

9

Routing policy:

select the forwarder with the lowest

rank

If

V2(t)

<

V1(t)

, node S will select

2 as the next forwarder

S

2

1

0.5

0.8

0.8

0.8

0.9

DSlide10

Characterization of congestion measure10

Vi (t) are solutions to “

Bellman-Ford”

like equation

Local congestion

Expected

delay from neighbor

j

Effective draining time for node

i

Link success probability between

i

and

j

Queue length at node

iSlide11

Congestion Granularity

CDP uses instantaneous queue lengths to detect congestionCDP tracks the state of the networkChanges in congestion can be rapid Due to channel fluctuations and traffic variationsTackles the short term instantaneous congestionDifferent from “minimum delay routing” [Gallager84]

Uses long term congestion for routing

Different from congestion control at transport layer

Transport layer caters to statistical, long term congestion

11Slide12

Mac Layer

Compute V and broadcast control packet

Control packets from neighbors

Phy layer

RX

TX

Data Packets

Probe Packets

Data Packet

Probe Packets

CDP Design

Determine Link probability

Determine

V(t

)

200ms

Delayed feedback

12Slide13

Link determinationLink quality updates (estimation of pij’s)

Active probing + passive probingBroadcast-based active probing Passive probing Monitor received data packets

Detect missing data packets

using sequence numbers

Estimate the link

success probabilities

This reduces probing overhead

A

B

Capacity

overhead - dedicated probe packets

13Slide14

1

D

N

i

j

Scheduling

802.11 Mac

Node

i

selects one node

j

as a relay

Node

j

takes the responsibility of the packet

14

j

Control packets computing

V

are critical

Can get stuck due to congestion

Assign higher priority than data packets

Use driver’s priority queuing Slide15

Formation of loopsCurse of decentralized implementationTemporary build-up of queue at F causes packets to loop in C-B

Counting to infinity situationSplit horizon rule is used to prevent routing loopsNode C knows B’s next hop is CC sets VB = ∞, C does not learn from BA node advertises routes as unreachable to the node through which they were learned

Design Issues (1)

15

F

B

D

A

CSlide16

Design Issues (2) Delay vs. link probability curveAssumption: Non linear, difficult to characterizePacket are dropped after RETRY_MAX

Chosen a naïve solutionNeglect links with p < ThresholdChoose Threshold = 0.616

?

Link probability

Media access

delay

0

1Slide17

Experimental Setup

17

~100

mt

.

Indoor Network of

12

Alix

nodes in Atkinson Hall (Calit2

) 500Mhz CPU and 1GB flash.

MAC:

Atheros

IEEE 802.11 chipset (5212) using the

MadWifi

-NG driver

.

Multiple

hops (5 hops maximum)

13

dBm

transmit power

4 neighbors average

Performance Measures

Mean end to end delay

Mean delivery

ratio

Comparative protocols:

SRCR

Back

Pressure (BP)

Enhanced Back

Pressure (E-BP)Slide18

Canonical Example

18

50x

100x

SRCR

CDP

10

BP

14

16

17Slide19

Examples(1)

19

10

14

17

16Slide20

Examples(2)

20

10

14

17

4

8Slide21

Examples(3)

21

Single-hop flows: No gain

Mean Delay

Delivery ratioSlide22

22

¥

Inter-flow interference

Increase in potential number of transmitters

Nothing for free

11

17Slide23

23

Intra-flow interference

(interference artificially increased by increasing power at interfering nodes)

Multiple paths create contention

Potential to degrade the performance

Nothing for free

¥

13

7

11

5Slide24

Rule of Thumb CDP shows improvement when Network provides diversity

Many multi-hop routes are availableMany short length flows congest long length flowInterflow and intraflow interference do not dominate the congestion24Slide25

Performance Results (Parameters)UDP trafficRandom source destination pairs Multiple concurrent flows (2 flows)

Choose UDP rates randomly 10 random topologies80 sample pointsPlot CDF of mean delay of CDP - mean delay of candidate protocol Ratio of delivery ratios 25Slide26

Performance Results

26

High Load

Low Load

30%Slide27

CDP: Summary

CDP exploits the congestion diversity in the networkCDP achieves high gain in the delay and delivery ratio for UDP trafficCDP shows up to 50x gain over SRCR and 100x over backpressure routing

Routing Policy

Delay Performance

Low Traffic

High Traffic

Shortest-Path

Good delay performance

Poor delay

Backpressure

Poor delay performance

Guaranteed bounded delay

Poor delay in practice

CDP

Good delay performance

Good delay in practice

27Slide28

28

15

13

5

11

7

4

8

6

17

16

10

14

0.1xsec

Next hop for CDP

Current Focus (Interaction with TCP)

TCP performance hurts

Self interference

Reordering of packets

Fast retransmit

Which factor is more

dominant

?Slide29

Investigate the impact of CDP on TCP and mixed trafficDetect the cause of performance loss for TCPTackle reordering issue in TCP

Include prioritized MAC in CDP Change backoff proportional to the congestion

Immediate Goals

29Slide30

Develop multi-rate CDP utilizing multiple PHY ratesDefine congestion measure for multi-rate CDP

Implement opportunistic version of CDP on testbed Modify MadWifi driver’s 802.11 Mac layer

Immediate Goals

30

S

1

2

D

3

0.8

0.8

0.8

0.8

0.2

D

SSlide31

Ongoing work

31Objective

Timeline

CDP performance

Reordering of TCP (July)

MIXED/TCP (August)

Summer

Infocom

,

ToN

Prioritized MAC

October

Fall

Multi rate CDP

December

Fall

ORCD (Opportunistic CDP)

Mac layer modification (August)

Analysis/Delay performance (Feb)

Spring 10

IMC/

MobicomSlide32

32

Research Objectives

To design an opportunistic routing policy that enhances

throughput by

minimizing the expected number of

transmissions

32

To design

routing

policy that

exhibits good

delay

performance

and

study in a

testbed

.

2

Distributed Adaptive Opportunistic Routing (D-AdaptOR)

Opportunistic routing

Optimality

1Slide33

33

With no link probability information, can we use only “DATA PACKETS” to efficiently route?Adaptive Opportunistic Routing Protocol

D-AdaptOR

S

D

?

Opportunistic setting

(no congestion consideration)

CDP : Explicit probe packets to determine topologySlide34

Opportunistic Routing

Routing decisions are made based on actual transmission outcomes via a three-way handshake.It exploits the broadcast nature of wireless transmissions.

Path diversity is available in many settings.

A

long hop

might be rare but it happens.

S

1

2

D

3

0.8

0.8

0.8

0.8

0.2

Routing Cycle

1

Transmission

(T)

2

Acknowledgment (A)

3

Relaying (R)

Slot n

(T)

(R)

(A)

34Slide35

Contribution

When topology known, there exists an algorithm with optimal performance

EXOR,

GeRaF

, SR

The

knowledge of channel statistics is not known or

erroneous

D-AdaptOR: Distributed Adaptive Opportunistic Routing protocol

D-AdaptOR has zero initial knowledge

Explores new opportunities

Decisions based on current information

Balances exploration with exploitation

Goal : Design

an

algorithm independent of channel knowledge while guaranteeing good performance

35Slide36

36Network Model

Multi-hop ad-hoc network of nodesNodes

connected by unreliable links

Transmission Model:

Node

i

broadcasts a packet

c

i

: fixed cost of transmission

Set of nodes S successfully receive with probability

P(S|i

)

Error-free

Ack

packets

Slotted time:

Transmission and relay selection in one time slot

Only

one

node is selected as forwarder

1

D

2

N

i

2

SSlide37

:

Sequence of relay nodes

:

Time of termination

r

:

If packet is dropped before reaching

destination

then

0

reward, else reward

R

0

R

Termination Reward-Total Cost

A measure of distance

The Setup

Expected Reward

1

2

o

D

37Slide38

Problem (P)

Maximize the expected average per packet reward when the link probabilities are not known

Based on received

acks

, the next decision is to

choose an

ack-ed

neighbor as the next relay,

retransmit,

drop the packet altogether

Reward for packet

m

`

38

Optimality CriterionSlide39

Main Result

The D-AdaptOR achieves optimalityWhen,

Channel statistics are ergodic

Control packets are reliable

Theorem:

D-AdaptOR

maximizes

the average per packet expected reward

D-AdaptOR

maximizes

39Slide40

Routing Cycle

1

Transmission (T)

2

Ack (A)

3

Relaying (R)

4

Adaptive Update (U)

Receiving nodes acknowledge

Receiving nodes feedback their estimated reward

Transmitter or when selecting the relay (randomized)

With high

prob

: Choose the node with best

estimate

With diminishing

prob

: Choose randomly among neighbors

Adaptive

Update

Transmitter updates estimated reward

explores

exploits

AdaptOR

Operation

i

1

2

D

40Slide41

AdaptOR: Summary

41

Developed distributed opportunistic routing algorithm

D-AdaptOR

D-AdaptOR

is optimal (under technical conditions)

D-AdaptOR

outperformed other existing protocols in realistic simulations

D-AdaptOR

achieved the exploration vs. exploitation tradeoff

D-AdaptOR

can be implemented in off the shelf hardware

Relevant Papers

A. Bhorkar, M.

Naghshvar

, T.

Javidi

and B.

Rao

"An Adaptive Opportunistic Routing Scheme for Wireless Ad-hoc Networks ," submitted to

IEEE Trans. on Networking.

A. Bhorkar, M.

Naghshvar

, T.

Javidi

and B.

Rao

”AdaptOR An Adaptive Opportunistic Routing Scheme for Wireless Ad-hoc Networks “, ISIT 09

A. Bhorkar, M.

Naghshvar

, T.

Javidi

and B.

Rao

”Exploration

vs

Exploitation in wireless Ad-hoc networks ,” CDC 09Slide42

Develop learning algorithms for time varying network dynamics

Optimize over exploration and increase convergence rateUse “Regret” as a measure of optimalityImplement D-AdaptOR on test-bed

Future

work

Relevant Papers

A. Bhorkar, T.

Javidi

, A.

Snoeren

, Achieving congestion diversity in wireless Ad-hoc networks, submitted to

Mobicom

10

A. Bhorkar, T.

Javidi

, A.

Snoeren

, Achieving congestion diversity in wireless Ad-hoc networks, in preparation

42

Objective

Timeline

No regret routing

Nearly complete

Asilomar

10