1. Topics in Database Systems: Data Management in Peer-to-Peer Systems
Search in Unstructured P2p

2. Topics in Database Systems: Data Management in Peer-to-Peer Systems
D. Tsoumakos and N. Roussopoulos, “A Comparison of Peer-to-Peer Search Methods”, WebDB03

3. Overview
Centralized
Constantly-updated directory hosted at central locations (do not scale well, updates, single points of failure)
Decentralized but structured
The overlay topology is highly controlled and files (or metadata/index) are not placed at random nodes but at specified locations
Decentralized and Unstructured
peers connect in an ad-hoc fashion
the location of document/metadata is not controlled by the system
No guarantee for the success of a search
No bounds on search time
No maintenance cost
Any kind of query (not just single key or range queries)

4. Flooding on Overlays
xyz.mp3 ?
xyz.mp3

5. Flooding on Overlays
xyz.mp3
xyz.mp3 ?
Flooding

6. Flooding on Overlays
xyz.mp3
xyz.mp3 ?
Flooding

7. Flooding on Overlays
xyz.mp3

8. Search in Unstructured P2P
Must find a way to stop the search: Time-to-Leave (TTL)
Exponential Number of Messages
Cycles (?)
Note: cycles can be detected but not avoided

9. Search in Unstructured P2P
BFS vs DFS
BFS better response time, larger number of nodes (message overhead per node and overall)
Note: search in BFS continues (if TTL is not reached), even if the object has been located on a different path
Recursive vs Iterative
During search, whether the node issuing the query directly contacts others, or recursively.
Does the result follows the same path?

10. Iterative vs. Recursive Routing
Iterative: Originator requests IP address of each hop
Message transport is actually done via direct IP
Recursive: Message transferred hop-by-hop
K V
K V
K V
K V
K V
K V
K V
K V
K V
K V
K V
retrieve (K1)

11. Search in Unstructured P2P
Two general types of search in unstructured p2p:
Blind: try to propagate the query to a sufficient number of nodes (example Gnutella)
Informed: utilize information about document locations (example Routing Indexes)
Informed search increases the cost of join for an improved search cost

12. Blind Search Methods
Gnutella:
Use flooding (BFS) to contact all accessible nodes within the TTL value
Huge overhead to a large number of peers +
Overall network traffic
Hard to find unpopular items
Up to 60% bandwidth consumption of the total Internet traffic

13. Free-riding on Gnutella [Adar00]
24 hour sampling period:
70% of Gnutella users share no files
50% of all responses are returned by top 1% of sharing hosts
A social problem not a technical one
Problems:
Degradation of system performance: collapse?
Increase of system vulnerability
“Centralized” (“backbone”) Gnutella  copyright issues?
Verified hypotheses:
H1: A significant portion of Gnutella peers are free riders
H2: Free riders are distributed evenly across domains
H3: Often hosts share files nobody is interested in (are not downloaded)

14. Free-riding Statistics - 1 [Adar00]
H1: Most Gnutella users are free riders
Of 33,335 hosts:
22,084 (66%) of the peers share no files
24,347 (73%) share ten or less files
Top 1 percent (333) hosts share 37% (1,142,645) of total files shared
Top 5 percent (1,667) hosts share 70% (1,142,645) of total files shared
Top 10 percent (3,334) hosts share 87% (2,692,082) of total files shared

15. Free-riding Statistics - 2 [Adar00]
H3: Many servents share files nobody downloads
Of 11,585 sharing hosts:
Top 1% of sites provide nearly 47% of all answers
Top 25% of sites provide 98% of all answers
7,349 (63%) never provide a query response

16. Free Riders File sharing studies Lots of people download
Few people serve files
Is this bad?
If there’s no incentive to serve, why do people do so?
What if there are strong disincentives to being a major server?

17. Simple Solution: Thresholds
Many programs allow a threshold to be set
Don’t upload a file to a peer unless it shares > k files
Problems:
What’s k?
How to ensure the shared files are interesting?

18. Categories of Queries [Sripanidkulchai01]
Categorized top 20 queries

19. Popularity of Queries [Sripanidkulchai01]
Very popular documents are approximately equally popular
Less popular documents follow a Zipf-like distribution (i.e., the probability of seeing a query for the ith most popular query is proportional to 1/(ialpha))
Access frequency of web documents also follows Zipf-like distributions  caching might also work for Gnutella

20. Caching in Gnutella [Sripanidkulchai01]
Average bandwidth consumption in tests: 3.5Mbps
Best case: trace 2 (73% hit rate = 3.7 times traffic reduction)

21. Topology of Gnutella [Jovanovic01]
Power-law properties verified (“find everything close by”)
Backbone + outskirts
Power-Law Random Graph (PLRG):
The node degrees follow a power law distribution:
if one ranks all nodes from the most connected to the least connected, then
the i’th most connected node has ω/ia neighbors,
where w is a constant.

22. Gnutella Backbone [Jovanovic01]

23. Why does it work? It’s a small World! [Hong01]
Milgram: 42 out of 160 letters from Oregon to Boston (~ 6 hops)
Watts: between order and randomness
short-distance clustering + long-distance shortcuts
In 1967, Stanley Milgram conducted a classic experiment where he instructed randomly chosen people in Nebraska to pass letters to a selected target person in Boston, using only intermediaries who were known to one another on a first-name basis. He found that it only required a median of six steps for the letters to reach their destination, giving rise to “six degrees of separation” and the “small-world effect.”
Duncan Watts and Steven Strogatz extended this work in 1998 with an influential paper in Nature that described small-world networks as an intermediate state between regular graphs and random graphs. Small-world graphs maintain the high local clustering of regular graphs (as measured by the clustering coefficient, the proportion of a nodes linked to a given node which are also linked to each other) but also have the short pathlengths of random graphs. They can be regarded as locally clustered graphs with shortcuts scattered in.
Freenet networks can be shown to be small-world graphs (next slide).
Regular graph:
n nodes, k nearest neighbors
 path length ~ n/2k
4096/16 = 256
Rewired graph (1% of nodes):
path length ~ random graph
clustering ~ regular graph
Random graph:
path length ~ log (n)/log(k)
~ 4

24. Links in the small World [Hong01]
“Scale-free” link distribution
Scale-free: independent of the total number of nodes
Characteristic for small-world networks
The proportion of nodes having a given number of links n is: P(n) = 1 /n k
Most nodes have only a few connections
Some have a lot of links: important for binding disparate regions together
A key characteristic of small-world graphs is the “scale-free” link distribution, which has no term related to the size of the network, and thus applies at all scales from small to large. This distribution can be seen in Freenet. The nodes at the top left, with few connections, are the local clusters while the nodes at the bottom right, with lots of connections, provide the shortcuts that tie the network together. The outlier at far right is the group of nodes whose datastores are completely filled, with 250 entries – with larger datastores, this column shifts further to the right.

25. Freenet: Links in the small World [Hong01]
P(n) ~ 1/n 1.5
A key characteristic of small-world graphs is the “scale-free” link distribution, which has no term related to the size of the network, and thus applies at all scales from small to large. This distribution can be seen in Freenet. The nodes at the top left, with few connections, are the local clusters while the nodes at the bottom right, with lots of connections, provide the shortcuts that tie the network together. The outlier at far right is the group of nodes whose datastores are completely filled, with 250 entries – with larger datastores, this column shifts further to the right.

26. Gnutella: “New” Measurements
[1] Stefan Saroiu, P. Krishna Gummadi, Steven D. Gribble:
A Measurement Study of Peer-to-Peer File Sharing Systems,
Proceedings of Multimedia Computing and Networking (MMCN)
2002, San Jose, CA, USA, January 2002.
[2] M. Ripeanu, I. Foster, and A. Iamnitchi.
Mapping the gnutella network: Properties of large-scale peer-to-peer systems and implications for system design.
IEEE Internet Computing Journal, 6(1), 2002
[3] Evangelos P. Markatos,
Tracing a large-scale Peer to Peer System: an hour in the life of Gnutella,
2nd IEEE/ACM International Symposium on Cluster Computing and the Grid, 2002.
[4] Y. HawatheAWATHE, S. Ratnasamy, L. Breslau, and S. Shenker.
Making Gnutella-like P2P Systems Scalable. In Proc. ACM SIGCOMM (Aug. 2003).
[5] Qin Lv, Pei Cao, Edith Cohen, Kai Li, Scott Shenker:
Search and replication in unstructured peer-to-peer networks. ICS 2002: 84-95 

27. Gnutella: Bandwidth Barriers
Clip2 measured Gnutella over 1 month:
typical query is 560 bits long (including TCP/IP headers)
25% of the traffic are queries, 50% pings, 25% other
on average each peer seems to have 3 other peers actively connected
Clip2 found a scalability barrier with substantial performance degradation if queries/sec > 10:
10 queries/sec
* 560 bits/query
* (to account for the other 3 quarters of message traffic)
* simultaneous connections
67,200 bps
10 queries/sec maximum in the presence of many dialup users
won’t improve (more bandwidth - larger files)

28. Gnutella: Summary Completely decentralized Hit rates are high
High fault tolerance
Adopts well and dynamically to changing peer populations
Protocol causes high network traffic (e.g., 3.5Mbps). For example:
4 connections C / peer, TTL = 7
1 ping packet can cause packets
No estimates on the duration of queries can be given
No probability for successful queries can be given
Topology is unknown  algorithms cannot exploit it
Free riding is a problem
Reputation of peers is not addressed
Simple, robust, and scalable (at the moment)

29. Lessons and Limitations
Client-Server performs well
But not always feasible
Ideal performance is often not the key issue!
Things that flood-based systems do well
Organic scaling
Decentralization of visibility and liability
Finding popular stuff (e.g., caching)
Fancy local queries
Things that flood-based systems do poorly
Finding unpopular stuff [Loo, et al VLDB 04]
Fancy distributed queries
Vulnerabilities: data poisoning, tracking, etc.
Guarantees about anything (answer quality, privacy, etc.)

30. Comparison
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31. Comparison
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32. Information Preservation Information Quality Trust
Security & Privacy
Issues:
Anonymity
Reputation
Accountability
Information Preservation
Information Quality
Trust
Denial of service attacks

33. ? title: origin of species author: charles darwin date: 1859
Authenticity
title: origin of species
author: charles darwin ?
date: 1859
body: In an island far,
far away ...
...

34. More than Just File Integrity
title: origin of species
author: charles darwin ?
date: 1859 00
body: In an island far,
far away ...
checksum

35. More than Fetching One File
T=origin
Y=?
A=darwin
B=?
T=origin
Y=1859
A=darwin
B=abcd
T=origin
Y=1800
A=darwin
Y=1859

36. Solutions
Authenticity Function A(doc): T or F
at expert sites, at all sites?
can use signature expert sig(doc) user
Voting Based
authentic is what majority says
Time Based
e.g., oldest version (available) is authentic

37. Trust computations in dynamic system Overloading good nodes
Issues
Trust computations in dynamic system
Overloading good nodes
Bad nodes can provide good content sometimes
Bad nodes can build up reputation
Bad nodes can form collectives
...

38. Back to searching

39. Blind Search Methods
Modified-BFS:
Choose only a ratio of the neighbors (some random subset)
Iterative Deepening:
Start BFS with a small TTL and repeat the BFS at increasing depths if the first BFS fails
Works well when there is some stop condition and a “small” flood will satisfy the query
Else even bigger loads than standard flooding
(more later …)

40. Two methods to terminate each walker:
Blind Search Methods
Random Walks:
The node that poses the query sends out k query messages to an equal number of randomly chosen neighbors
Each step follows each own path at each step randomly choosing one neighbor to forward it
Each path – a walker
Two methods to terminate each walker:
TTL-based or
checking method (the walkers periodically check with the query source if the stop condition has been met)
It reduces the number of messages to k x TTL in the worst case
Some kind of local load-balancing

41. Blind Search Methods
Random Walks:
In addition, the protocol bias its walks towards high-degree nodes (choose the highest degree neighbor)

42. Blind Search Methods
Using Super-nodes:
Super (or ultra) peers are connected to each other
Each super-peer is also connected with a number of leaf nodes
Routing among the super-peers
The super-peers then contact their leaf nodes

43. Blind Search Methods
Using Super-nodes:
Gnutella2
When a super-peer (or hub) receives a query from a leaf, it forwards it to its relevant leaves and to neighboring super-peers
The hubs process the query locally and forward it to their relevant leaves
Neighboring super-peers regularly exchange local repository tables to filter out traffic between them

44. Interconnection between the superpeers
Blind Search Methods
Ultrapeers can be installed (KaZaA) or self-promoted (Gnutella)
Interconnection between the superpeers

45. Informed Search Methods
Local Index
Each node indexes all files stored at all nodes within a certain radius r and can answer queries on behalf of them
Search process at steps of r, hop distance between two consecutive searches 2r+1
Increased cost for join/leave
Flood inside each r with TTL = r, when join/leave the network

46. Informed Search Methods
Intelligent BFS
query
... ?
Nodes store simple statistics on its neighbors:
(query, NeigborID) tuples for recently answered requests from or through their neighbors
so they can rank them
For each query, a node finds similar ones and selects a direction
How?

47. Informed Search Methods
Intelligent or Directed BFS
query
... ?
Heuristics for Selecting Direction
>RES: Returned most results for previous queries
<TIME: Shortest satisfaction time
<HOPS: Min hops for results
>MSG: Forwarded the largest number of messages (all types), suggests that the neighbor is stable
<QLEN: Shortest queue
<LAT: Shortest latency
>DEG: Highest degree

48. Informed Search Methods
Intelligent or Directed BFS
No negative feedback
Depends on the assumption that nodes specialize in certain documents

49. Informed Search Methods
APS
Again, each node keeps a local index with one entry for each object it has requested per neighbor –
this reflects the relative probability of the node to be chosen to forward the query
k independent walkers and probabilistic forwarding
Each node forwards the query to one of its neighbor based on the local index (for each object, choose a neighbor using the stored probability)
If a walker, succeeds the probability is increased, else is decreased –
Take the reverse path to the requestor and update the probability, after a walker miss (optimistic update) or after a hit (pessimistic update)