1. Early Measurements of a Cluster-based Architecture for P2P Systems
Yinglian Xie
Carnegie Mellon University
Balachander Krishnamurthy, Jia Wang
ATT Labs---Research

2. Motivation
Peer-to-peer(P2P) applications provide us with a new content service model
End-hosts self organized into an overlay network and share content with each other
For a wide deployment of P2P applications
We need a scalable content location and routing scheme in the application layer
We need to study and understand P2P traffic patterns
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3. Recent Work Existing approaches for content location Recent designs
Napster: uses a centralized server
Gnutella: relies on flooding of queries
Recent designs
Distributed indexing schemes based on hash functions
CAN, Chord, Pastry, Tapestry
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4. Our Work A Cluster-based architecture (CAP) for P2P systems
Example application: distributed search (support keyword searching)
Design: using network-aware clustering
Early measurements of CAP
trace analysis + simulations
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5. CAP System Design Network-aware clustering
B. Krishnamurthy and J.Wang. On Network-Aware Clustering of Web Clients. In proceedings of ACM Sigcomm, August 2000
An effective technique to group clients that are topologically close and under common administrative domain
Apply network-aware clustering to P2P applications
An additional level in the hierarchy
Less dynamism
More scalability
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6. CAP Architecture Three entities Two operations Clustering server
client
delegate
Clustering server
Three entities
Clustering server
Delegate
Client
Two operations
Node join and node leave
Query lookup
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7. Inter-cluster Routing
Each query has a maximum search depth
Each delegate keeps a neighbor list
Assigned randomly when the delegate joins the network
Updated gradually based on application requirements
Depth-first search among neighbors
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8. CAP Evaluation
Collect Gnutella traces, apply network-aware clustering in trace data analysis
To examine the potential advantage of using network-aware clustering
Trace-driven simulations
Measure CAP system performance based on real deployment (ongoing work)
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9. Collecting Gnutella Trace
A modified open source Gnutella client (gnut) to passively monitor and log all Gnutella messages
Location
Trace length
Number of IP addresses
CMU
10 hours
799,386
ATT
14 hours
302,262
ACIRI
6 hours
185,905
Location
Trace length
Number of IP addresses
CMU
89 hours
301,025
ATT
139 hours
261,094
UKY
96 hours
409,084
75 hours
292,759
WPI
10 hours
69,285
Table 1
Traces with unlimited connections
Table 2
Traces with limited connections
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10. Cluster Distribution CMU trace
5/24/2001 – 5/25/2001, 799,386 IP addresses, 45,129 clusters
Clustering helps reduce query latency by caching repeated queries
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11. Client and Cluster Distribution along Time
Network-aware clustering helps reduce dynamism in the P2P network
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12. Simulation Trace-driven simulation Performance metrics
Use Gnutella trace to generate “join, leave, search”
Assume the query distribution follows the file distribution
Performance metrics
Hit rate
Overhead
Search Latency
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13. Hit Rate Use CMU trace 1,000 node stationary network 311 clusters
4,615search messages
3,793 unique files
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14. Overhead and Search Latency
Messages per search, forward operations per delegate
In Gnutella, overhead grows exponentially
In CAP, overhead grows linearly
Search Latency
Application level hop length
In CAP, search path length is short
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15. Summary
CAP is promising to increase stability and scalability of distributed applications
Ongoing work: We are implementing CAP, deploying it in machines around the world, and measuring the performance
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