Weak State Routing for Large Scale Dynamic Networks Utku Günay Acer, Shivkumar Kalyanaraman,...
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Transcript of Weak State Routing for Large Scale Dynamic Networks Utku Günay Acer, Shivkumar Kalyanaraman,...
Weak State Routing for Large Scale Dynamic Networks
Utku Günay Acer, Shivkumar Kalyanaraman, Alhussein A. Abouzeid
Rensselaer Polytechnic InstituteDepartment of Electrical, Computer & Systems Engineering
Which area should we NOT be working on in MOBICOM anymore?
Ans: Routing !
- Victor Bahl, Mobicom 2007 panel
Routing in Large-scale Dynamic Networks
Routing table entries: “state” = indirections from persistent names (ID) to locators
Due to dynamism, such indirections break
Problematic on two dimensions Dynamism/mobility =>
frequent update of state Dynamism + large scale
=> very high overhead, hard to maintain structure
We propose constructing routing table indirections using probabilistic and more stable state: WEAK STATE
Number of NodesN
ode M
obili
ty
A new class of State
Strong StateDeterministicRequires control
traffic to refreshRapidly
invalidated in dynamic environments
Weak StateProbabilistic
hintsUpdated locally
Exhibits persistence
STATEB
STATEB
Hard, Soft and Weak State
a b
INSTALL
STATEA
STATEA
REMOVE
Hard StateSoft State
UPDATE
Time elapsed since state
installed/refreshedWeak StateConfidence in
state information
Weak State is natural generalization of soft state
Random Directional Walks
Both used to announce location information (“put”) and forward packets (“get”)
Outline
Our Weak State RealizationDisseminating Information and
Forwarding PacketsSimulation ResultsDiscussion & Conclusion
An Instance of Weak State
The uncertainty in the mappings is captured by locally weakening/decaying the state
Other realizations are possibleProphet, EDBF etc…
{a,b,c,d,e,f}
Probabilistic in terms of membershi
p
SetofIDsGeoRegion
Probabilistic in terms of
scope
Consider a node aWhere is node a?
(i): It is in region ABwith probability 1
(ii) It is in region B with probability 2
(1 · 2)
Example: Weak State
128.113.
128.113.
128.113.62.
128.113.62.
128.113.50.
128.113.50.
xn
1x
2x
x
21
x
How to “Weaken” State?
Larger Geo-Region
Aggregation
SetofIDs -> GeoRegion
Aggregation: setofIDs
setofIDs: We use a Bloom filter, denoted by B.
m1 m2
….0 1 0 0 0 0 0 0 11 1 1 1 1
uhj(m1)
…. ….
k k
hi(m1)hj(m2)
hi(m2)
Decaying/Weakening the setofIDs
Randomly reset 1’s to 0. Same as EDBF [Kumar et al. Infocom’05]
Let (m)=i=1m B(hi(m))
Large (m) ! fresh mapping(m)/k is a rough measure of P{xm 2
A}
….0 1 0 0 0 0 0 0 11 1 1 1 1
hj(m) hk(m)h1(m) hi(m)
….0 1 0 0 0 0 0 0 11 1 1 1….0 1 0 0 0 0 0 0 11 1 1 1 1….0 1 0 0 0 0 0 0 11 1 1 1….0 1 0 0 0 0 0 01 0 1 1 1
Weakening State (Contd)
xn
2
uB
setofIDs small, time passes:Decay GeoRegion
xn
2
uB
xn
Either setofIDs large ORGeoRegion Large =>
Decay SetofIDs
Random Directional Walks
Both used to announce location information (“put”) and forward packets (“get”)
Dissemination/Proactive Phase: (put)
When a node receives a location announcement, it creates a ID-to-
location mapping aggregates this
mapping with previously created mappings if possible
A
B
C
Confidence0.71
Confidence0.84
Confidence1.0
Confidence0.71
Confidence0.84
Confidence0.71
Confidence0.84
Confidence0.71
Confidence0.71
Forwarding Packets(get)
Forwarding decision: similar to longest-prefix-match. “strongest semantics match” to decide how to bias the random walk.
Confidence0.71
Confidence0.84
Confidence1.0
1.0
Confidence0.71
Confidence0.84
Confidence1.0
1.0
Confidence0.71
Confidence0.84
Confidence1.0
S
D
A
B
C
E
WSR involves unstructured, flat, but scalable routing ; no flooding !
Simulation Objectives How does WSR perform?
Large-scale High Mobility
Comparisons: DSR: works well for small scale, high mobility GLS+GPSR: works well for large scale, low mobility
Short answer: 98%+ packet delivery despite large scale AND high mobility.
Tradeoffs: longer path lengths, (N3/2) state overhead
Simulation SetupBenchmarks
DSR and GLS-GPSRRandom waypoint mobility model with
vmin=5 m/s and vmax=10 m/sWSR is robust against dynamism (please see
the paper for details)Performance Metrics
Packet delivery ratioControl packet overheadNumber of states maintained Normalized path lengthEnd-to-end Delay
400 500 600 700 800 900 1000-0.2
0
0.2
0.4
0.6
0.8
1
1.2
Number of Nodes
Pac
ket
Dev
iver
y R
atio
WSR
GLS-GPSRDSR
Packet Delivery Ratio
GLS breaks down due to overheads
DSR only delivers a small fraction of
packets
WSR achieves high delivery ratio
Control Packet Overhead
400 500 600 700 800 900 10000
1000
2000
3000
4000
5000
6000
7000
Number of Nodes
Tot
al O
verh
ead
per
Sec
ond
(Num
ber
of P
acke
ts)
WSR
GLS-GPSR
Maintaining structure requires
superlinearly increasing
overhead in GLS
400 500 600 700 800 900 10000
0.5
1
1.5
2
2.5
3
3.5
4x 10
4
Number of Nodes
Tot
al N
umbe
r of
Map
ping
s/D
atab
ase
Ent
ries
Mai
ntai
ned WSR
GLS-GPSR
Number of States Maintained
The total state stored in the network increases as (N3/2) instead of
(NlogN)
Per-Node State Dynamics
0 100 200 300 400 500 600 700 800 900 10000
5
10
15
20
25
30
35
40
45
time (seconds)
Num
ber
of S
tate
s M
aint
aine
d
State generation rate matches state removal rate.
Distribution of Per-Node State in the Network
The states are well distributed with a C.o.V 0.101
20 25 30 35 40 45 50 550
20
40
60
80
100
120
Number of States
Num
ber
of O
ccur
renc
es
Normalized Path Length
400 500 600 700 800 900 10001
1.5
2
2.5
3
3.5
4
4.5
Number of Nodes
Nor
mal
ized
Pat
h Le
ngth
WSR
GLS-GPSRDSR
GLS sends packets only to destinations that are successfully located
Packets take longer paths with WSR
400 500 600 700 800 900 10000
10
20
30
40
50
60
70
Number of Nodes
End
to
End
Del
ay (
s)WSR
GLS-GPSRDSR
But, E2E Delay is Lower!
WSR uses random walks for discovery &
dissemination => end-to-end delay is smaller
Discussion/Future Work
Weak State Routing also relates toDTN routingUnstructured rare object recall in P2P
networksDistributed Hashing
Future work: Such extensions (especially DTNs)Theoretical analysis
Conclusion
Weak state is soft, updated locally, probabilistic and captures uncertainty
Random directional walks both for location advertisement and data forwarding.
WSR provides scalable routing in large, dynamic MANETs
WSR achieves high delivery ratio with scalable overhead at the cost of increased path length