1p2p, Fall 06
Topics in Database Systems: Data Management in Peer-to-Peer Systems
Search in Unstructured P2p
2p2p, Fall 06
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
3p2p, Fall 06
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)
4p2p, Fall 06
Flooding on Overlays
xyz.mp3 ?
xyz.mp3
5p2p, Fall 06
Flooding on Overlays
xyz.mp3 ?
xyz.mp3
Flooding
6p2p, Fall 06
Flooding on Overlays
xyz.mp3 ?
xyz.mp3
Flooding
7p2p, Fall 06
Flooding on Overlays
xyz.mp3
8p2p, Fall 06
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
9p2p, Fall 06
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?
10p2p, Fall 06
retrieve (K1)
K V
K V
K V
K V
K V
K V
K V
K V
K V
K V
K V
Iterative vs. Recursive Routing Iterative: Originator requests IP address of each hop
• Message transport is actually done via direct IPRecursive: Message transferred hop-by-hop
11p2p, Fall 06
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
12p2p, Fall 06
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
13p2p, Fall 06
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)
14p2p, Fall 06
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
15p2p, Fall 06
Free-riding Statistics - 2 [Adar00]
H3: Many servents share files nobody downloadsOf 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
16p2p, Fall 06
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?
17p2p, Fall 06
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?
18p2p, Fall 06
Categories of Queries [Sripanidkulchai01]
Categorized top 20 queries
19p2p, Fall 06
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
20p2p, Fall 06
Caching in Gnutella [Sripanidkulchai01]
• Average bandwidth consumption in tests: 3.5Mbps • Best case: trace 2 (73% hit rate = 3.7 times traffic reduction)
21p2p, Fall 06
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.
22p2p, Fall 06
Gnutella Backbone [Jovanovic01]
23p2p, Fall 06
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
Regular graph:
n nodes, k nearest neighbors
path length ~ n/2k
4096/16 = 256
Random graph:
path length ~ log (n)/log(k)
~ 4
Rewired graph (1% of nodes):
path length ~ random graph
clustering ~ regular graph
24p2p, Fall 06
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
25p2p, Fall 06
Freenet: Links in the small World [Hong01]
P(n) ~ 1/n 1.5
26p2p, Fall 06
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
27p2p, Fall 06
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* 4 (to account for the other 3 quarters of message traffic)* 3 simultaneous connections67,200 bps 10 queries/sec maximum in the presence of many dialup users won’t improve (more bandwidth - larger files)
28p2p, Fall 06
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)
240,26)1(**20
TTL
i
iCC
29p2p, Fall 06
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.)
30p2p, Fall 06
Comparison
Gnutella CAN Others?
Expressivness
Comprehensivness
Autonomy
Efficiency
Robustness
Topology pwr law
Data Placement arbitrary
Message Routing flooding
31p2p, Fall 06
Comparison
Gnutella CAN Others?
Expressivness
Comprehensivness
Autonomy
Efficiency
Robustness
Topology pwr law grid
Data Placement arbitrary hashing
Message Routing flooding directed
32p2p, Fall 06
Security & Privacy
• Issues:– Anonymity– Reputation– Accountability– Information Preservation– Information Quality– Trust– Denial of service attacks
33p2p, Fall 06
Authenticity
title: origin of species
author: charles darwin
date: 1859
body: In an island far,far away ...
...
?
34p2p, Fall 06
More than Just File Integrity
title: origin of species
author: charles darwin
date: 1859
body: In an island far,far away ...
checksum
? 00
35p2p, Fall 06
More than Fetching One File
T=originY=1800
A=darwin
T=originY=1859
A=darwin
T=originY=1859
A=darwin
T=originY=?
A=darwinB=?
T=originY=1859
A=darwinB=abcd
36p2p, Fall 06
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
37p2p, Fall 06
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• ...
38p2p, Fall 06
Back to searching
39p2p, Fall 06
Blind Search Methods
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 …)
Modified-BFS:
Choose only a ratio of the neighbors (some random subset)
40p2p, Fall 06
Blind Search MethodsRandom 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
41p2p, Fall 06
Blind Search Methods
Random Walks:
In addition, the protocol bias its walks towards high-degree nodes (choose the highest degree neighbor)
42p2p, Fall 06
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
43p2p, Fall 06
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
44p2p, Fall 06
Blind Search Methods
Ultrapeers can be installed (KaZaA) or self-promoted (Gnutella)
Interconnection between the superpeers
45p2p, Fall 06
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
46p2p, Fall 06
?query ...
Informed Search Methods
Intelligent BFS
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?
47p2p, Fall 06
• 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
?query ...
Informed Search Methods
Intelligent or Directed BFS
48p2p, Fall 06
Informed Search Methods
Intelligent or Directed BFS
• No negative feedback• Depends on the assumption that nodes specialize in certain documents
49p2p, Fall 06
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)