network-non-traditional-routing.ppt

Network Routing: Metrics and

Non-Traditional Routing

Y. Richard Yang

2/26/2009

2

Admin.

Project propose due: Friday

3

Recap: Key Problems

Forwarding and location management

Routing due to node mobility/wireless connectivity, link

connectivity/quality can be highly dynamic• need to design routing protocols that are effective in

handling dynamic topologies there can be interference among links and paths

• need good link performance metrics or scheduling

4

Recap: Routing Protocols

Proactive protocols distance vector

• e.g., DSDV link state link reversal

• e.g., partial link reversal, TORA

Reactive protocols DSR AODV

A

ED

CB

F

2

2

13

1

1

2

53

5

5

Recap: ETX

ETX: The predicted number of data transmissions required to successfully transmit a packet over a link

Link loss rate = p Expected number of transmissions

Problems of using ETX in 802.11 networks?

Problems of ETX

ETX does not handle multirate 802.11 networks

ETX does not work out well when nodes have multiple radios working on different channels

6

7

Extending ETX: Multirate

In a multirate environment, need to consider link bandwidth: packet size = S, Link bandwidth = B each transmission lasts for S/B

“Routing in Multi-radio, Multi-hop Wireless Mesh Network,” Richard Draves, Jitendra Padhye, and Brian Zill. Mobicom 2004.

8

Extending ETX: Multirate

Add ETTs of all links on the path Use the sum as path metric

Interpretation: pick a path with the lowest total network occupation time Q: under what condition is SETT total

network occupation time?

9

Extending ETX: Channel Diversity with Multiradio

10

Impact of Interference

Interference reduces throughput throughput of a path is lower if many links

are on the same channel path metric should be worse for non-diverse

paths

11

Combining Link Metric into Path Metric: Proposal 2

Group links on a path according to channel assumes links on the same channel interfere

with one another pessimistic for long paths

Add ETTs of links in each group Find the group with largest sum (BG-ETT)

this is the “bottleneck” group too many links, or links with high ETT (“poor

quality” links) Use this largest sum as the path metric

Lower value implies better path

12

BG-ETT Example

Path Blue Sum Red Sum

BG-ETT Throughput

All-red

1 Blue

Red-Blue

0 5.33 ms 5.33 ms 1.5 Mbps

1.33 ms 4 ms 4 ms 2 Mbps

2.66 ms 2.66 ms 2.66 ms 3 Mbps

13

BG-ETT May Select Long Paths

14

Path Metric: Putting it all together

SETT favors short paths BG-ETT favors channel diverse paths

β is a tunable parameter Higher value: more preference to channel

diversity Lower value: more preference to shorter paths

15

Implementation and such

Measure loss rate and bandwidth loss rate measured using broadcast probes similar to

ETX• updated every second

bandwidth estimated using periodic packet-pairs updated every 5 minutes

Implemented in a source-routed, link-state protocol, Multi-Radio Link Quality Source Routing (MR-LQSR) nodes discover links to its neighbors, measure

quality of those links link information floods through the network

• each node has “full knowledge” of the topology• sender selects “best path”• packets are source routed using this path

16

Evaluations

17

Media Throughput (Baseline, single radio)

18

Media Throughput (Baseline, two radios)

19

Impact of β value

Channel diversity is important; especially for shorter paths

Summary

Link metrics are still an active research area

20

21

Summary: Traditional Routing So far, all routing protocols in the

framework of traditional wireline routing a graph representation of underlying network

• point-to-point graph edges with costs select a lowest-cost route for a src-dest pair commit to a specific route before forwarding

Problems: don’t fully exploit path (spatial) diversity and wireless broadcast opportunities

Outline

Admin. Link metrics Non-traditional routing

22

23

Motivating Scenario: I

Assumes independent loss Tradition routing has to follow one pre-committed route Does not allow exploration of opportunities

24

Motivating Scenario: II

Traditional routing picks a single route, e.g., src -> B -> D -> dst packets received off path are useless

However, one should take advantage of transmissions that reach unexpectedly far or unexpectedly short

25

Motivating Scenario: III

A sends 1 packet to B; B sends packet 3 to A

If R has both packets 1 and 3, it can combine them and explore coding and broadcast nature of wireless

A BR

Outline


motivation network coding: exploiting network

broadcast

26

27

Coding

We have covered source coding (FEC, compression)

The new approach uses opportunistic network coding

goal: increase the amount of information that is transported

28

Opportunistic Coding

Outline



broadcast opportunistic routing: ExOR

29

30

Opportunistic Routing (ExOR)

Group packets into batches

Instead of choosing a fix sequential path (e.g., src->B->D->dst), the source chooses a list of forwarders (a forwarder list in the packets) using ETX-like metric a background process collects ETX

information via periodic link-state flooding

Forwarders are prioritized by ETX-like metric to the destination

31

ExOR: Forwarding

The highest priority forwarder transmits when the batch ends

The remaining forwarders transmit in prioritized order each forwarder forwards packets it receives yet not

received by higher priority forwarders status collected by batch map

A nodes stops sending the remaining packets in the batch if its batch map indicates over 90% of this batch has been received by higher priority nodes the remaining packets transferred with traditional

routing

32

Batch Map: Example

Batch map indicates, for each packet in a batch, the highest-priority node known to have received a copy of that packet

33

Example: Timeline

Forwarder list: N24(dst), N20, N18, N11, N8, N17, N13, N5(src)

34

Evaluations

65 Node pairs 1.0MByte file

transfer 1 Mbit/s 802.11

bit rate 1 KByte packets EXOR bacth size

100

1 kilometer

35

Evaluation: 2x Overall Improvement

Median throughputs: 240 Kbits/sec for ExOR, 121 Kbits/sec for Traditional

Throughput (Kbits/sec)

1.0

0.8

0.6

0.4

0.2

00 200 400 600 800C

um

ula

tive F

ract

ion o

f N

ode P

air

s

ExORTraditional

36

25 Highest throughput pairs

Node Pair

Thro

ughput

(Kbit

s/se

c)

0

200

400

600

800

1000 ExORTraditional Routing

1 Traditional Hop

1.14x

2 Traditional Hops1.7x

3 Traditional Hops2.3x

37

ExOR uses links in parallel

Traditional Routing3 forwarders

4 links

ExOR7 forwarders

18 links

38

ExOR moves packets farther

ExOR average: 422 meters/transmission Traditional Routing average: 205 meters/tx

Fract

ion o

f Tra

nsm

issi

ons

0

0.1

0.2

0.6 ExORTraditional Routing

0 100 200 300 400 500 600 700 800 900 1000

Distance (meters)

25% of ExOR transmissions

58% of Traditional Routing transmissions

39

Comments

Pros takes advantage of link diversity (the

probabilistic reception) to increase the throughput

does not require changes in the MAC layer can cope well with unreliable wireless

medium and mobility Cons

maybe hard to scale to a large network overhead in packet header (batch info) batches increase delay

Outline



broadcast opportunistic routing: ExOR opportunistic routing: MIXIT

40

Mesh Networks Borrowed the Internet API

Network

Forward correct packets to destination

PHY/LL Deliver correct packets

S

R1

R2

D

99% (10-3

BER)

99% (10 -3 BER)

Wireless Naturally Provides Reliability Across Links

0%

0%

Even 1 bit in 1000 incorrect Packet loss of 99%

S

R1

R2

D

99% (10-3

BER)

99% (10 -3 BER)

Implication

0%

0%

Link by link reliability 50 transmissions

Loss

Loss

S

R1

R2

D

99% (10-3

BER)

99% (10 -3 BER)

Wireless Naturally Provides Reliability Across Links

0%

0%

Spatial diversity: Even if no correct packets, every bit is likely received correctly at some node

Exploit wireless characteristics 3 transmissions

Current contract 50 tx Low throughputExploit wireless characteristics 3 tx High throughput

Useful with High Quality Links?R1

R2

R3

R4

Sa

Pb

Db

Da

Sb

Pb

Pa

Pa

Pa

Pb

1%

2%

1%

3%

0%

0%

0%

0%

Loss

Loss

Loss

Loss

Useful with High Quality Links?R1

R2

R3

R4

Sa

Pb

Db

Da

Sb

Pb

Pa

Pa

Pa

Pb

1%

2%

1%

3%

0%

0%

0%

0%Current contract Inhibits concurrency

Exploit wireless characteristics Enables high concurrency

Current Contract

Limits throughput, inhibits concurrency

PHY + LL

Deliver correct symbols to higher layer

Network

Forward correct symbols to destination

PHY + LL

Deliver correct packets

Network

Forward correct packets to destination

High throughput, high concurrency

New Contract Exploiting Wireless Characteristics

MIXIT• New contract between layers to harness

wireless characteristics• Novel symbol-level network code that

scalably routes correct symbols• High concurrency MAC• Implementation and evaluation

• 3-4x gain over shortest path routing• 2-3x gain over packet-level opp. routing

How does a Router Identify Correct Symbols?• PHY already estimates a confidence for every

decoded symbol [JB07]• PHY + LL delivers high confidence symbols to

network layer

PHY Confidence

Packet

PHY + LL


Network


What Should Each Router Forward?

R1

R2

DSP1P2

P1P2

P1P2

What Should Each Router Forward?

R1

R2

DSP1P2

But overlap in correctly received symbols Potential solutions1)Forward everything Inefficient2)Coordinate Unscalable

P1P2

P1P2

P1P2

P1P2

Forward random combinations of correct symbols

R1

R2

DSP1P2

MIXIT Prevents Duplicates using Symbol Level Network Coding

P1P2

P1P2

1s

…

…R1

R2

D

2s2

1

7s

2s

2

7

…

1s

…

…

2s

Routers create random combinations of correct symbols

2

1

9s

5s

5

9

…


R1

R2

D2

1

7s

2s

…

2

1

9s

5s

…

21 s,sSolve 2

equations

Destination decodes by solving linear equationsRandomness prevents duplicates without co-ordinationRandomness prevents duplicates without co-ordination


1s

…

…R1

R2

D

2s2

1

7s

2s

2

7

…

1s

…

…

2s

Routers create random combinations of correct symbols

15s

5

0

…


R1

R2

D2

1

7s

2s

…

15s …

21 s,sSolve 2

equations

Destination decodes by solving linear equations

Symbol Level Network Coding • No duplicates Efficient • No coordination Scalable

Symbol Level Network Coding • No duplicates Efficient • No coordination Scalable


Destination needs to know which combinations it received

21 9s5s

21 0s5s

21 9s0s

(if both symbols were correct)

(if only s1 was correct)

(if only s2 was correct)

Nothing (if neither symbol was correct)


Use run length encoding

5

9

Original Packets Coded Packet

0

9




9

5




0

5




Run length encoding efficiently expresses combinations

Run length encoding efficiently expresses combinations



Routers May Forward Erroneous Bits Despite High Confidence

MIXIT has E2E error correction capability!

Symbol-LevelNetwork CodingECC Data

MIXIT’s Error Correcting Code (ECC)1.Routers are oblivious to ECC2.Optimal error correction capability3.Rateless

Decode ECCData

PHY + LL


Network


Source Destination

High Concurrency MAC

• Each node maintains a map of conflicting transmissions

• Map is based on empirical measurements and built in distributed, online manner

w & x NO!w & u YES!

xu w

Evaluation

• Implementation on GNURadio SDR and USRP• Zigbee (IEEE 802.15.4) link layer• 25 node indoor testbed, random flows• Compared to:

1. Shortest path routing based on ETX2. MORE: Packet-level opportunistic routing

Throughput (Kbps)

CD

F

Throughput increase: 3x over SPR,

2x over MORE

Throughput increase: 3x over SPR,

2x over MORE

Throughput Comparison

2.1x3x

Shortest PathMOREMIXIT

Throughput (Kbps)

CD

FWhere do the gains come from?


Take concurrency away from MIXIT

Where do the gains come from?

1.5x

Without concurrency, 1.5x gain over MOREWithout concurrency, 1.5x gain over MOREThroughput (Kbps)

CD

F

Shortest PathMORE

MIXIT withoutconcurrency

Take concurrency away from MIXIT

Where do the gains come from?

Throughput (Kbps)

CD

F

MIXIT

Gains come from both moving to the symbol level and high

concurrency

Gains come from both moving to the symbol level and high

concurrency

Shortest PathMORE

MIXIT withoutconcurrency

Multiple Flows


No. of concurrent flows

Avg.

Net

wor

k Th

roug

hput

(Kbp

s)

MORE/SPR: Higher congestion Lower concurrency

MIXIT: Higher congestion High concurrency

MORE/SPR: Higher congestion Lower concurrency

MIXIT: Higher congestion High concurrency

79

Geographical Routing

81

Motivations

A need for geographical based distribution e.g., sensornets and location-based services

Reducing state overhead the routing protocols we discussed so far

may not scale to large-scale networks• maintain (end-to-end) routing states for each

destination• thus overhead proportional to network size

82

GeoRouting: Greedy Distance

S D

Closest to D

A

-Find neighbors who are the closest to destination D-To make progress, the chosen neighbor should be closer to destination

83

Geographical Routing

Each node only needs to keep state for its neighbors

Beaconing mechanism each node broadcasts its MAC and position to minimize costs: piggybacking

84

Greedy Routing Not Always Works

D

85

Greedy Routing Works Well in Dense Networks

If node density is high, it is a low probability event for the region to have no nodes

Greedy fails if no node in this region

D

86

Dealing with Void: Right-Hand Rule

Right-hand rule: When arriving at node x from node y, the next edge traversed is the next one sequentially counterclockwise about x from edge (x,y)

87

Right Hand Rule on Convex Subdivision

Applying the right hand rule to convex subdivision (namely a planar graph where every internal face is a polytope): first remove the edges crossing the line from source to destination, and then apply the right hand rule

s t

If not convex subdivision, removing crossing edges may not work

88

Right-Hand Rule Does Not Work Well with Cross Edges

u

z

w

D

x

v

x originates a packet to u

Right-hand rule results in the long detour x-u-z-w-u-x

89

Removing Cross Edges

u

z

w

D

x

v

Remove (w,z) from the graph

Right-hand rule results in the route x-u-z-v-D

90

How to Make a Graph Planar?

Convert a connectivity graph to planar non-crossing graph by removing “bad” edges

make sure the original graph will not be disconnected

two types of planar graphs: • Relative Neighborhood Graph (RNG)• Gabriel Graph (GG)

91

Relative Neighborhood Graph

Edge uv can exist only if there does not exist another node w inside the intersection of the two circles centered at u and v with radius d(u, v) i.e., no w such that d(w, u) < d(u, v) and d(w, v) < d(u, v)

Or equivalently w u, v: d(u,v) ≤ max[d(u,w),d(v,w)]

not empty remove uv

92

Gabriel Graph

An edge (u,v) exists between vertices u and v if no other vertex w is present within or on the circle whose diameter is uv.

w u, v: d2(u,v) < [d2(u,w) + d2(v,w)]

Not empty remove uv

93

• 200 nodes

• randomly placed on a 2000 x 2000 meter region

• radio range of 250 m

•Bonus: remove redundant, competing path less collision

Full graph GG subset RNG subset

Examples

94

Properties of GG and RNG

RNG is a sub-graph of GG because RNG removes more

edges

GG is a planar graph and thus RNG is also planar

Connectivity if the original graph is

connected, RNG is also connected

RNG

GG

95

Connectedness of RNG Graph

Key observation any edge on the minimum

spanning tree of the originalgraph is not removed

Assume (u,v) is such an edge but removed in RNG due to w

u v

w

96

Delaunay Triangulation

Let disk(u,v,w) be a disk defined by the three points u,v,w

The Delaunay Triangulation (Graph) There is a triangle of edges

between three nodes u,v,w iff the disk(u,v,w) contains no other points

Properties of the Delaunay Triangulation graph it is the dual of the Voronoi

diagram DT graph is planar

97

Some Interesting Properties

Since the MST(V) is connected and the DT(V) is planar, all the planar graphs above are connected and planar

98

Final Algorithm: Greedy Perimeter Stateless Routing (GPSR)

Maintenance all nodes maintain a single-hop neighbor

table Use RNG or GG to make the graph planar

Routing use greedy forwarding whenever possible resort to perimeter routing when greedy

forwarding fails and record current location Lc

resume greedy forwarding when we are closer to destination than Lc

99

a

d

b

c

S

e

f

D

a

S

c

e

D

Example

For details about GPSR algorithm, please [GPSR].

d

100

Evaluations

50, 112, and 200 nodes with 802.11 WaveLAN radios

Maximum velocity of 20 m/s 30 CBR traffic flows, originated by 22

sending nodes Each CBR flows at 2 Kbps, and uses 64-

byte packets

101

Packet Delivery Success RateVery dense network: 20 neighbors

102

Routing Protocol Overhead

103

Routing State

State per router for 200-node GPSR node stores state for 26 nodes on

average in pause time-0

104

Path Length

105

Worst Case of GeoRouting in Terms of Hop Count

A worst case scenario destination is central node source is any node on ring any spine can go to

middle O(c) nodes along ring and

O(c) nodes along each spine

Best path length: O(c)+O(c)

Geographic routing: Test O(c) spines of length

O(c) Cost O(c2) instead of O(c)

106

Geographic Routing

107

Pros and Cons of GPSR

Pros: low routing state and control traffic scalable handles mobility well

Cons: planarized graph is hard to guarantee under mobility location might not be available everywhere (we will

see the problem next week) geographic distance does not correlate well with

network proximity

108

Why Geographic Routing Without Location?

Location is hard to get GPS takes power, doesn’t work indoors,

difficult to incorporate in small sensors the network localization problem is difficult

True location may not be useful if there are obstacles

S DA

B

In the “connectivity” space, B is closer to destination!!

109

Overview

Objective: assign “virtual” coordinates to nodes so that the coordinates are computed efficiently, routing works well using the computed

coordinates Progress in three steps

the perimeter nodes and their locations are known

the perimeter nodes are known but not their coordinates are not known

nothing is known We cover the first two steps

110

The Perimeter Nodes and Their Locations Are Known

Image a rubber band from each node to each (connected) neighbor

The force of a rubber band is proportional to its length, directedto the neighbor

111

The Perimeter Nodes and Their Locations Are Known

The equilibrium is achievedwhen the position p of a node is equal to the average of its neighbors

where n is number of neighbors

Algorithm each node sends its position

to its neighbors a node updates its new position to be the

average of those of its neighbors

n

pppp i

i 0)(

n

tptp i

)()1(

112

Perimeter Nodes Are Known (True Positions)

3200 nodes; 64 perimeter nodes on the boundary

113

Perimeter Nodes Are Known (10 iterations)

Internal nodes initialized as the center of the square

114



115



116

Routing Performance

32000 packets with random source-destination pairs

Success rate

Average path length

True position

0.989 16.8

Virtual position

0.993 17.1

Success rate: using (distance) greedy routing

117

Two More Scenarios

Success rate: 0.981Avg. path length: 17.3

Success rate: 0.99Avg. path length: 17.1

118

Weird Shapes

119

The Perimeter Nodes Are Known But Their Locations Are Not Known

Assume the distance between two nodes is the (minimum) number of hops to go from one to the other

Distances can be derived by flooding the network each perimeter node sends a HELLO

message with a hop counter of 0 when seeing a message from a perimeter node with

a lower hop counter, a node increases the counter by 1 and forwards it

120

Virtual Coordinates by Triangulation

Each perimeter node solves the minimization problem:

Detail

nodesperimeter ,

2ji, )-(dmin

jiji pp

21

22

2

2

221

2

UX

Uof columns 2first the:

eseigen valu 2largest the:

111 ;/

][ where, Compute

V

U

V

UVUH

JJDH

],...,[eneeIJ

dDD

T

T

ij

121

Convergence and Performance

One iteration: success rate = 0.992; avg. path length = 17.2Ten iterations: success rate = 0.994; avg. path length = 17.2

Backup Slides

123

Improving Resiliency

If perimeter vector is inaccurate, it results in inconsistent information

Augment with two beaconing nodes which provide two coordinate axes special perimeter nodes or run leader

election to select the two nodes

Origin is chosen as the center of gravity of all perimeter nodes more robust to lost information

124

Selecting Perimeter Nodes

Rule: A node is a perimeter node if: It is farthest away from the first beacon

node among all its one-hop neighbors

125

Perimeter Node Detection

126

Convergence and Performance

Ten iterations: success rate = 0.996; avg. path length = 17.3

127

Projecting on Circle After First Computation

Circle: center is center of gravity; radius is average of distance of the perimeter nodes to the CG

Motivation: maintain a consistent coordinate space

128

Virtual Polar Coordinate Space Each node is assigned a label

label is number of hops to root and virtual angle range

Discussion: how to do routing?

129

Build the Tree

Root node starts as root broadcasts “level 0”

A non-root node1. receives message “level n” – marks parent2. broadcasts message saying level “n+1”

All subtrees report size back to parent.Root does assignment of virtual angles.

Question: what about the issues of the described algorithm?

130

Motivation for a Better Metric

131

Implementation and such

Modify DSDV or DSR

Example evaluation: in DSDV w/ ETX, route table is a snapshot

taken at end of 90 second warm-up period in DSR w/ ETX, source waits additional 15

sec before initiating the route request

132

ETX Performance

DSDV DSR

Date post:	21-May-2015
Category:	Documents
Upload:	networkingcentral
View:	699 times
Download:	0 times

network-non-traditional-routing.ppt

Documents