Count / Top-k Continuous Queries on P2P Networks
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Count / Top-k Continuous Queries on P2P Networks
01/11/2006
Outline
Problem Definition P2P Architecture Count Top-K Experiment Setup Future Work
Streaming Data in P2P
P2PDynamic changing topology, large scale, …
Streaming dataContinuous, unbounded, rapid, time-varying,
noise P2P + Streaming data
Dynamic in both data and topology
Objective and Goal
Objective Issue a continuous query to estimate count and
top-K Goal
Lower down the communication costLightweight maintenanceApproximated answersAn adaptive and progressive approach
Naïve approach
Flooding the overlay continuousPros
Closer to the exact answer
Cons Network congestion Still non-real time
The State-of-the-Art
CountFocus on one-time answer in P2PDeal with streaming data only
Top-KP2P environment without streaming dataDistributed environment not P2P
P2P architecture
AssumptionHierarchical P2P (Focused)
Super-peer hierarchical structure Query issuer is a super-peer Super peer connect with other super peers Each peer belongs to only one super peer
Pure unstructured P2P
Big picture
Group
Accumulate information within a group based on the constraintand statistics
Set Constraint
Report changes
Approximated answer
Group in hierarchical P2P
Issuer
Coordinator
Peer
Group in hierarchical P2P
3
1
4
2
Group in hierarchical P2P
4
3
3
1
4
2
Group in hierarchical P2P
4
3
3
1
4
2
After partition
Group1
Group3Group2
,01,... 0ii N C
Assume we have N objects and K Groups after partition
,
:1, ...,
:1, ...,
: Count at each peeri j
i N
j K
C
User-specified Epsilon
Group1
Group3Group2
User-specifiedε(Precision)
Consider a group
P4
P1
P3
P2
CoordinatorNode
Objects
O1
O2
O3
Each node maintain the distribution information of owning objects
P2
P4
P1
P3
object
Rate
#
R1
R2
R3
R4
At initial - Polling
P4
P1
P3
P2
CoordinatorNode
At initial - Polling
P4
P1
P3
P2
CoordinatorNode
Information at coordinator after polling
object
#
22
2633
P4
P3P2
P1
Statistics information
object
# P1 P2 P3 P4 ΔO1 1/1 6/6 10/10 5/5 22O2 11/11 13/13 5/5 9/7 36O3 15/15 6/6 3/3 9/9 33R 0.3 0.2 -0.05 0.6T 15 15 17 13
22
2633
Updated time stamp
Maximum changing rate(+/-) of objects in each peer
Change value for each objectLatest real value
Estimated value
Update to Coordinator
(Δ11, Δ21, Δ31)
T2
(Δ12, Δ22, Δ32)
(Δ13, Δ23, Δ33)
Calculate Count
( 1) ( ),0 ,0 ,
1
Kl li i i j
j
C C
Redistribute Epsilon
,0( , , )i if C
wi=Max(Δi)/Cx,0 where x is the i-index of Max(Δi)δi=wiεCx,0/ ∑wi
Visiting sequence
P4
P3P2
P1
Pick those peers would violate δ
Update information
Group
P1 P2 P3 P4 ΔO1 1/1 6/6 10/10 8/8 -O2 11/11 11/11 5/5 6/6 -O3 15/15 5/5 3/3 11/11 -R 0.3 0.4 -0.05 0.2T 15 30 17 33
For those nodes not being visited
Group
P1 P2 P3 P4 ΔO1 1/2 6/6 10/9 8/8 25O2 11/13 11/11 5/4 6/6 34 O3 15/18 5/5 3/2 11/11 36 R 0.3 0.4 -0.05 0.2T 15 30 17 33
Un-notified Leave
P1
Ping
P1 is dead
Remove P1’s information
P4
P3P2
Experiment Setup
Generate synthetic data set by statistics distribution for Streaming dataLife time of peers
MetricsMessage sizeCommunication costResponse latencyResult accuracy
Top-K
Use Regression to predicate the reasonable trend of changesOnce a updated result is required, Super Peer
only need to ask those doubtful peers for doubtful objects
Update its counting list, and return the top k objects
Future Work
Connect and recommend latent good friends for each user Good friends: the ones with the same interests (behaviors)
Exploiting current connecting peers to discover good friends bit by bit
Design a system that could make clusters reflecting current interests of individual peers and connecting them together based on their similarity by using user’s social network
Advantages
Reduce search time and diminish query traffic by using friends list
By utilizing their different strength of arcs/edges/ties = friendshipness, social networks exceed random-walk networks in quickly finding target objects
Example
Level 1
Level 2
Example
has larger weight than
Score(Ni)
Score(Ni)
1 1( ) ( , ) ( )i i i iscore N sim N N score N
Similarity
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