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Gabriel Kliot, Roy Friedman Technion – Israel Institute of Technology
and Chen Avin – Ben Gurion University, Israel
Probabilistic Quorum Probabilistic Quorum
Systems in Wireless Ad Systems in Wireless Ad
Hoc NetworksHoc Networks
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How Can One Find Data?
Centralized directory
Flooding lookup requests or advertisements expensive
DirectoryServer
DataOwner
DataClient
Advertise Lookup
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How Can One Find Data?
Publishing advertisements to a subset P and looking up the data in a subset L such that P and L intersect This is known as quorums
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Quorum System: A set of subsets over a universe U such that
for any Q1,Q2 in Q, Q1∩Q2≠Ф
Bi-quorum System: A couple of sets of subsets (Q1,Q2) over a
universe U such that for any Q1 in Q1 and Q2 in Q2, Q1∩Q2≠Ф
Quorums and Bi-Quorums
Majority
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Probabilistic Quorums [Malkhi, Reiter, Wool, Wright ‘01]
In probabilistic quorums, the intersection property is only ensured with some probability
The members of the probabilistic quorum are selected on each quorum access using an access strategy For example, pick nodes at random
This ensures intersection with probability Suitable for dynamic ad hoc
networks
nl2le
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Our Contributions Different accesses strategies, with varying
trade-offs Mix and Match theorem – we can mix them in
different ways, that guarantee intersection Asymmetric bi-quorum systems
Explore various combinations Theoretically, based on Random Geometric Graph
model By simulations
Along the way, some theoretical results about Random Walks in Random Geometric Graphs
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n nodes 2-dimensional unit torus [0,1]2
Uniform placement Edge between 2 nodes within Euclidian distance r No geographic knowledge We use Random Geometric Graph only for
performance analysis The correctness is ensured on any topology
Formal Network Model: 2D Random Geometric Graph
),(2 rnG
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Access Strategies in MANET
UglyUglyBadBadGoodGoodAccess CostAccess Cost(Random Geometric
Graph)
RoutingMembership / sampling service
Early halting
(if accessed serially)
RANDOMRANDOM
ln( )
nQ
n
Partial Cover Time
High
Crossing TimeCrossing Time
Revising nodes along the path
No routing
Early haltingPATH (RW)PATH (RW) , for / 2Q Q n
MAC broadcast:
•Not EE
•Low bandwidth
No Early halting
Multiple replies
No routingDepends on TTL
(no fine grained control over the cost)
FLOODINGFLOODING Q
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Mix and Match Known result [Malkhi, Reiter, Wool, Wright]:
If two quorums of size are chosen uniformly at random, then their non-intersection probability is
Our result: We show that if one of these quorums is
chosen uniformly at random, then the other quorum can be chosen in any way (including deterministically)
nl
2le
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Mix and Match Specifically, assume Qa and Qb, Qa chosen
uniformly at random and Qb chosen arbitrarily, but in a non-adversarial manner (e.g., using the PATH access strategy)
Lemma 1:
Lemma 2: In order to have intersection with probability 1-ε, the sizes of Qa and Qb must satisfy
For example, for an intersection probability of 0.9, we can pick
n
QbQa
a eQQ b
)Pr(
)/1ln( nQQ ba
2 1.15a bQ n Q n
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Optimized RANDOM strategy
Adding Cross Layer Optimization Similar to RANDOM, except that a lookup
request that passes through any intermediate node does a local lookup as well
Benefit comes from the mix&match result That is, as soon as the first lookups visit
nodes, it is likely that the object will be found Typically, after picking only a few nodes to visit
nl
125 10 15 20 25 30 35 40 45 500
5
10
15
20
25
30
35
40
45
50
RW TTL
Num
ber
of d
iffer
ent n
odes
vis
ited
by R
W
800 NODES - Simple RW
800 NODES - UNIQUE RW
Optimized PATH strategy - Unique PATH
By remembering the path, we can avoid revisiting nodes and speed up the walk
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Comparing the Access StrategiesComparing the Access Strategies
Q n
NoNoYesYes**
MultipleMultipleOneOne**One**# Replies
NoNoNoYesYes
Advertise Cost
YesEarly Halting
One
NoLookup Routing
Combined Cost
Lookup Cost
FLOODINGRANDOM
Advertise FLOODINGRANDOM-OPT
RANDOMLookup
nln( )
n
n
ln( )
n
n
ln( )
n
n ln( )
n
n
n n
FLOODING
nNo
Combined Cost
PATH PATH
PATH
Yes
One
No
PATH
***
***ln( )
n
n
ln( )
n
n
Yes
One
ln( )
n
n
** If accessed serially *** this is a lower bound, also validated by simulations
ln( )n n
Yes**
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Simulation setup
Simulations on JIST/SWANS http://jist.ece.cornell.edu/
Network sizes: 50, 100, 200, 400, 800 Random Waypoint mobility model
Speed between 0.5-2 m/s (walking speed) Average pause time 30 s
Transmission range ~ 220m Average number of neighbours davg=10 10 runs per data point, 1000 sec
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Simulations Scenarios 100 advertisements to a RANDOM quorum
of size nodes
1000 lookups 4 strategies:
RANDOM, RANDOM-OPT, UNIQUE-PATH, and FLOODING
On a hit, a reply was sent to the originator
Each hop is counted as one message
Hit ratio means the number of successful lookups for objects that were published
Corresponds to intersection probability
n2
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Results of RANDOM and RANDOM-OPT
Theory works… A hit ratio of 0.9 was obtained with a
quorum size of With 800 nodes, the quorum size is 33
The number of messages per lookup behaved as
RANDOM RANDOM-OPT
But, the overall communication cost was greatly affected by routing overhead, even in RANDOM-OPT
1.15 n
ln( )
nQ
nln( )Q n
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# Lookup Messages for RANDOM-OPT
2 4 6 8 10 12 140
50
100
150
200
250
300
RANDOM-OPT lookup Quorum size
Sen
t loo
kup
mes
sage
s pe
r lo
okup
50 NODES100 NODES200 NODES400 NODES800 NODES
Mobile network
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Total # Messages for RANDOM-OPT
2 4 6 8 10 12 140
500
1000
1500
2000
2500
3000
3500
4000
4500
RANDOM-OPT lookup Quorum size
Tot
al s
ent l
ooku
p m
essa
ges
per
look
up
50 NODES100 NODES200 NODES400 NODES800 NODES
This includes the cost of routing in a mobile network
195 10 15 20 25 30 35 40 45 50 55 60
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
UNIQUE-PATH lookup Quorum Size
HIT
RA
TIO
50 NODES100 NODES200 NODES400 NODES800 NODES
Hit Ratio for UNIQUE-PATH
For N=400, |lookup _Q|=23~1.15*sqrt(400)
guarantees intersection of 0.9 – like in theory
Mobile network
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# Lookup Messages for UNIQUE-PATH
5 10 15 20 25 30 35 40 45 50 55 600
5
10
15
20
UNIQUE-PATH lookup Quorum Size
Sen
t loo
kup
mes
sage
s pe
r lo
okup
50 NODES100 NODES200 NODES400 NODES800 NODES
No routing overhead here!
Number of messages is smaller than quorum size!
Due to early halting.
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Hit Ratio for FLOODING
1 1.5 2 2.5 3 3.5 4 4.5 50
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
FLOODING lookup Quorum TTL
HIT
RA
TIO
50 NODES100 NODES200 NODES400 NODES
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1 1.5 2 2.5 3 3.5 4 4.5 50
10
20
30
40
50
60
FLOODING lookup Quorum TTL
Sen
t loo
kup
mes
sage
s pe
r lo
okup
50 NODES100 NODES200 NODES400 NODES800 NODES
# Lookup Messages for Flooding
No routing overhead here too
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Simulation summary
50 (600 with routing)
35 (140 with routing)
RANDOM_OPTRANDOM_OPT
15*14mobile
15*14staticFLOODINGFLOODINGUNIQUE-PATHUNIQUE-PATH
400 nodes #lookup msgs that guarantee 0.9 intersection Including reply
Flooding is sent by broadcast Hidden overheads
Flooding does not allow fine grained control If we want to increase the intersection probability, we must
increase TTL, which will increase the #msgs significantly
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Conclusions Examined various combinations of access
strategies for probabilistic quorums in MANETs
RANDOM RANDOM-OPT PATH UNIQUE-PATH FLOODING
Showed that it is possible to obtain efficient probabilistic quorums
In particular using asymmetric combinations Using Random walks
More about handling failures and dynamism in the paper
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Future Directions
Shared objects (with linearizable semantics)
Pub/sub
Distributed search