Transcript [PowerPoint presentation]

Maximizing Remote Work in
Flooding-based P2P Systems
Qixiang Sun
Neil Daswani
Hector Garcia-Molina
Stanford University
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P2P File Sharing
Internet
• Gnutella, KaZaA, etc.
2
Architecture
• Super-node network with flooding-based search
Search Query
3
Problem
?
• Accept new queries from local clients
• Handle remote queries from other super-nodes
Where is the balance?
4
Problem (2)
• Objective: Remote Work
– process as many queries from other nodes as
possible.
Query
0
1
0
1
0
1
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Remote work done
Problem (3)
Where is the optimal?
Number of new queries injected
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Simple Model
Capacity C
Handles remote
queries
r
Super-nodes
operate in rounds
Accepts new queries
from local clients
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When Overloaded
• Choose queries with the
highest TTL first
?
• Ties can be broken
randomly
 Has a steady state and
is optimal in remote work
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Example
• 3 super-nodes with TTL = 1
A
B
r=
1
?
3
local
C
neighbor 1
neighbor 2
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Example (2)
• 6 super-nodes with TTL = 1
r=
1?
3
r=
1?
4
r=
1?
2
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Solution
• 6 super-nodes with TTL = 1
2
4
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Solution (2)
• 6 super-nodes with TTL = 1
3
2
4
4
3
{ 2, 2, 3, 3, 4, 4 }
2 4 7
2
r=
1
3
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Another Example
• 5 super-nodes with TTL = 2
4
3
5
5
4
{ 3, 4, 4, 5, 5 }
3+4=7>5
r=
1
4
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Intuition
Unsaturated
Saturated
...
2
...
5
7
r=
1
6
10
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Intuition (2)
Unsaturated
Saturated
...
2
...
5
Loss
7
?
r== 1
7
10
Gain
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Different r
Spare capacity
• Each super-node could use a different r
 More work done in the network!
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Example
• Star topology with TTL = 1
0
Identical r = 0.5
Remote work = 3.5 C
0
1
0
0
Different r
Remote work = 6 C
0
0
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Penalty of using identical r
...
D1
...
Di
Dn
Maximum remote work is at most n C
1
Pick r =
 all nodes saturated
D1
1
 remote work = n C (1 )
D1
1
penalty is
D1
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Penalty of using identical r(2)
1
• How big is
?
D1
D1  TTL + 1
In practice:
D1  50

penalty is less than 2%
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Solving for different r
w1
0
w2
0
1
0
0
w3
w4
0
0
Similar to finding
the dominating
set for the graph
Minimize sum of all weights
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Why?
Unsaturated
...
Saturated
...
Boost unsaturated nodes
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Future Directions
• Nodes of different capacities
• Incremental algorithm for computing r at
each node
• An incentive mechanism so that each
node will forward neighbors’ queries
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Conclusion
• Controlling rate of query injection leads to
better efficiency
• Solutions for finding the optimal rate
For other P2P related work, google for “Stanford Peers”
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