1
Information Retrieval in Peer-to-Peer Systems
Dept. of Computer Science Engineering. _at_
University of California - Riverside

• Demetrios Zeinalipour-Yazti

M.Sc. Thesis Defense Monday, May 5, 2003Surge
349 1200-100 PM
Thesis Committee Dr. Dimitrios Gunopulos,
Chairperson Dr. Vana Kalogeraki Dr. Chinya V.
Ravishankar
http//www.cs.ucr.edu/csyiazti/msc.html
2
Presentation Outline

• Introduction Motivation.
• Search Techniques for P2P systems
• The Intelligent Search Mechanism
• PeerWare Simulation Infrastructure
• Experimental Evaluation.
• Conclusions Future Work.

3
Introduction to Peer-to-Peer

• Peer-to-Peer Computing definition
• Sharing of computer resources and information
  through direct exchange

• Clients (downloaders) are also servers
• Clients may join or leave the network at any time
  gt highly fault-tolerant but with a cost!
• Searches are done within the virtual network
  while actual downloads are done offline (with
  HTTP).

4
Introduction to Peer-to-Peer

• Peer-to-Peer (P2P) systems are increasingly
  becoming popular.
• P2P file-sharing systems, such as Gnutella,
  Napster and Freenet realized a distributed
  infrastructure for sharing files.
• Traditionally, files were shared using the
  Client-Server model (e.g. http). Not scalable
  since they are centralized services.
• P2P uncover new advantages in simplicity of use,
  robustness, self organization and scalability.

5
Information Retrieval in P2P

• Problem
• How to efficiently retrieve Information in P2P
  systems where each node shares a collection of
  documents?

• Documents consists of keywords.
• Resembles Information Retrieval but resources are
  distributed now.
• Primary Data Structures such as Global Inverted
  Indexes cant be maintained efficiently.

6
Solutions for P2P Information Retrieval

• 1) Centralized Approaches
• Centralized Indexes
• e.g. Napster, SETI_at_HOME
• 2) Purely Distributed Approaches
• Each node has only local knowledge.
• I.R is done using Brute force mechanisms
• e.g. Gnutella, Fasttrack (Kazaa)
• 3) Hybrid Approaches
• One or more peers have partial indexes of the
  contents of others.
• e.g. Limewire's Ultrapeers

Centralized Index
1) Upload Index
2) Query/QueryHit
3) Download (offline)
1
2
3
1) Connect
2) Query/QueryHit
3) Download (offline)
1,2
3
1) Connect
2) Intelligent Query/QueryHit
3) Download (offline)
1,2
3
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Motivation

• On 1st June we crawled the Gnutella P2P Network
  for 5 hours with 17 workstations.
• We analyzed 15,153,524 query messages.
• Observation High locality of specific queries.
• We try to exploit this property for more
  efficient searches?

8
Presentation Outline

• Introduction Motivation.
• Search Techniques for P2P systems
• The Intelligent Search Mechanism
• PeerWare Simulation Infrastructure
• Experimental Evaluation.
• Conclusions Future Work.

9
Search Techniques for P2P systems

• Breadth-First Search (Gnutella)
• Idea Each Query Message is propagated along all
  outgoing links of a peer using TTL
  (time-to-live).
• TTL is decremented on each forward until it
  becomes 0
• Technique for I.R in P2P systems such as
  Gnutella.
• Highlights
• The physical network comes to its knees
• Long Delays for search results.

P2P Network N
A
QUERY
1
QUERYHIT
2
Peer q
Peer d
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Search Techniques for P2P systems

• Modified Random BFS
• V. Kalogeraki, D. Gunopulos, D.
  Zeinalipour-Yazti . CIKM2002
• Idea Each Query Message is forwarded to only a
  fraction of outgoing links (e.g. ½ of them).
• TTL is again decremented on each forward until it
  becomes 0.
• Highlights
• Fewer Messages but possibly less results
• This algorithm is probabilistic.
• Some segments may become
• unreachable

unreachable
B
A
QUERY
1
P2P Network N
QUERYHIT
2
C
Peer d
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Search Techniques for P2P systems

• Searching Using Random Walkers
• Q. Lv et al P. Cao, E. Cohen, K. Li, and S.
  Shenker. ICS2002
• Idea Each Query Message is forwarded to 1
  neighbor
• With k walkers after T steps we reach the same
  nodes as 1 walker after kT steps. (They use 16-64
  walkers)
• Highlights
• Network Traffic reduced (from BFS) by 2 orders of
  magnitude
• Increases the user-perceived delay (from 2-6 hops
  to 4-15 hops)
• This algorithm is probabilistic and the
  likelihood to locate the objects depends on the
  network topology.

Peer d
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Search Techniques for P2P systems

• 4. Using Randomized Gossiping to Replicate Global
  State F.M Cuenca-Acuna, Thu D. Nguyen HPDC-12
• Idea PlanetP uses Bloom Filters to propagate
  summary indexes of the contents of a Peer.
• Bloom Filters are used for Membership Queries
• Highlights
• Not Scalable (Technique works well
• for lt10000 nodes)
• No Data Replication Required
• False Positives are a function of m,n,k
• and can be kept small

D d
,d
,...,d

000
1
2
n
001
1
h
(d
)
010
1
1
011
m
h
(d
)
2
1
100
1
h
(d
)
3
1
101
d1?
110
h
(d
)
1
4
1
111
1
An 8-bit bloom filter w/ 4 hash functions
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Search Techniques for P2P systems

• 5. Searching using Local Indices Arturo Crespo
  and Hector Garcia-Molina, ICDCS 2002.
• Idea Create indices which contain statistics
  that reveal the direction towards the
  documents.
• Types of Proposed Indices
• Compound Routing Index (CRI) metricnumber of
  documents
• Hop-Count Routing Index (HRI) maintain a CRI for
  k hops,
• Exponentially Aggregated Index (ERI) Apply some
  cost formula on HRI to shrink HRIs size.
• Highlights
• Not Scalable, Expensive Routing Updates but
  better than replicating data indexes.
• Assumes static environment but No Data
  Replication Required

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Search Techniques for P2P systems

• 6. Directed BFS and the gtRES Heuristic 1/2
  Beverly Yang and Hector Garcia-Molina, ICDCS
  2002.
• Proposed Techniques
• Directed BFS based on aggregate statistics (e.g.
  num of results a peer returned, shortest queue,
  forwarded the most data)
• Iterative Deepening, until Z results are returned
• Local Indexes, each node maintains the actual
  index over the data of peers r hops away.
• Their experiments deploy the Direct BFS
  techniques by attaching nodes to the Gnutella
  Network.
• The gtRES Heuristic is shown to be working well.

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Search Techniques for P2P systems

• Directed BFS and the gtRES Heuristic 2/2
• The gtRES Heuristic is optimized to find Z
  documents efficiently for some user defined Z.
• gtRES works well because
• It captures stable/large network segments.
• Potentially less overloaded peers
• gtRES is a quantitative approach
• Drawback gtRES doesnt route queries to most
  relevant content

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Search Techniques for P2P systems

• 7. Depth-First-Search and Freenet
• I. Clarke O. Sandberg, B. Wiley, and T.W. Hong,
  LNCS 2009
• Idea Objects are Hashed and route the hash of a
  query based on the key closeness in a DFS
  manner.
• Highlights
• Uses caching of key/object for future requests.
• Data Replication along the QueryHit path provides
  Availability
• Anonymity of Searcher and Publisher.
• Drawbacks i) Searches ONLY based on Object
  Identifier.
• ii) The user-perceived delay is high

S
B
replicated
B
A
fileA
QUERY
h(A)
1
Search A
C
result
S
2
Peer
q
R
original fileA
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Search Techniques for P2P systems

• 8. Consistent Hashing and Chord
• Ion Stoica et al. SIGCOMM 2001
• Idea Objects/Nodes are hashed with m-bit
  identifier and organized in a virtual ring.
  Object lookup is achieved in O(logN).
• Highlights
• Consistent Hashing achieves (i) Good Load
  Balancing of keys (ii) Little object/key movement
  in case of node join/leave .
• Drawbacks i) Searches ONLY based on Object
  Identifier
• ii) Data Movement may be a big overhead.

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Presentation Outline

• Introduction Motivation.
• Search Techniques for P2P systems
• The Intelligent Search Mechanism
• PeerWare Simulation Infrastructure
• Experimental Evaluation.
• Conclusions Future Work.

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Intelligent Search Mechanism ISM

• Introduction
• Idea Each Query Message is forwarded
  intelligently based on what queries a peer
  answered in the past.
• Components of ISM (for each node u)
• Profile Mechanism, for each neighbor N(u).
• Peer Ranking Mechanism, for ranking peers locally
  and send a search query only to the ones that
  most likely will answer.
• Similarity Function, for finding similar search
  queries.
• Search Mechanism, for propagating queries based
  on local indexes

A
QUERY
1
profiles
QUERYHIT
2
?
Peer d
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Intelligent Search Mechanism ISM

• Components of ISM
• a) Profile mechanism.
• Maintains a list of past queries routed through
  that host.
• Every time a QueryHit is received the table is
  updated
• The profile manager uses a Least Recently Used
  policy to keep most recent queries in repository.
• Profiles are kept for neighbors only so the cost
  for maintaining this cost is O(Td), T is a
  limiting factor per profile, d is the degree of a
  node

Size Td

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Intelligent Search Mechanism ISM

• Components of ISM
• b) The RelevanceRank Peer Ranking Metric.
• Before forwarding a Query Message a peer performs
  an on-the-fly ranking of its peers to determine
  the best paths.
• We use the Aggregate Weighted Similarity of peer
  Pi to a query q, computed by a peer Pl as

2
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Intelligent Search Mechanism ISM

• Components of ISM
• c) Similarity Function The cosine similarity.
• Assume that L is a set of all words (in Profile
  Manager)\
• e.g. Lelections, bush, clinton, super, bowl,
  san, diego, ,italy, earthquake, disaster
• We define an L-dimensional space where each
  query is a vector.
• If qitaly disaster gt q (vector of q)
  0,0,0,,1,0,1
• Recall that we have a vector for each qi stored
  in the Profile Manager ( i.e. qi )

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Intelligent Search Mechanism ISM

• Components of ISM
• d) Search Mechanism
• Utilizes the Peer Ranking Mechanism to forward
  Queries to nodes that will potentially contain
  the info we are looking for

Peer d
profiles
?
QUERY
1
?
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Intelligent Search Mechanism ISM

• Breaking cycles with Random Perturbation
• Suppose that nodes answers to conjunction of
  q-terms
• Suppose that query q has no answer from A,B,C
  or D.
• and that one of them answered to similar q in
  the past
• ? Query q fails to explore the segment through E
• Random Perturbation adds one additional random
  message

25
Presentation Outline

• Introduction Motivation.
• Search Techniques for P2P systems
• The Intelligent Search Mechanism
• PeerWare Simulation Infrastructure
• Experimental Evaluation.
• Conclusions Future Work.

26
PeerWare Simulation Infrastructure

• Introduction
• PeerWare is our distributed middleware
  infrastructure that allows us to benchmark
  various Query Routing Algorithms.
• It is deployed on a network of 50 workstations
• It uses Public/Private Keys and SSH to connect to
  the networked hosts.
• It is implemented in JAVA and consists of
  approximately 10000 lines of code.

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PeerWare Simulation Infrastructure

• Why real middleware and not simulations?
• Many properties such as network failures, dropped
  queries may reveal interesting and unknown
  patterns.
• In a real middleware we are able to measure the
  actual time to satisfy queries.
• Finally there are no assumptions (network delays
  etc) which are typical in simulation
  environments
• The Anthill Project (Univ. of Bologna) uses a
  similar approach to investigate properties of the
  Freenet algorithm.

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PeerWare Simulation Infrastructure

• PeerWare Components
• dataGen The Dataset Generator
• graphGen The Network Graph Generator
• dataPeer The Data Node
• searchPeer The Search Node
• Other Administrative Components
• netLaucher Shell script that launches Network
• netStats Shell script that provides statistics
• graphPlot Shell script that plots Graphs based
  on generated results.

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PeerWare Simulation Infrastructure

• 1) dataGen Component
• dataGen is the Dataset Generator which generates
  documents about specific documents
• (each peer can have some specialized knowledge)
• It uses the REUTERS News Agency dataset (22,531
  documents).
• It groups documents by various properties
• Date, Topics, Places, People, Orgs, Companies
• In our experiments we use the Places attribute
  and generate 104 countries.

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PeerWare Simulation Infrastructure

• 2) graphGen Component
• graphGen is topology generator
• Currently it generates Random Topologies given
  parameters such as degree, IPs, ports
• It generates with graphViz visualizations of the
  generated topologies.

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PeerWare Simulation Infrastructure

• 3) dataPeer Component
• dataPeer is a P2P client that maintains an XML
  repository of documents.
• It uses the PDOM-XQL engine to query its
  documents.
• It pre-establishes connections to other peers
  with persistent TCP connections

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PeerWare Simulation Infrastructure

• 4) searchPeer Component
• searchPeer is a P2P client that connects to a
  PeerWare Network and performs unstructured
  queries.
• Keywords are sampled from within the dataset
• It logs statistics such as query response time,
  nodes answered to a node etc.

33
Presentation Outline

• Introduction Motivation.
• Search Techniques for P2P systems
• The Intelligent Search Mechanism
• PeerWare Simulation Infrastructure
• Experimental Evaluation.
• Conclusions Future Work.

34
Experimental Evaluation

• Introduction
• We create a distributed Newspaper application
• We use a Random Network of 104 peers
• Each peer has documents for 1 country
• The average degree of a node is 7 log2100
  (connected graph)
• We perform two series of experiments
• 10x10 sequential queries with a delay of 4 sec.
• 400 random queries with a delay of 4 sec.
• We compare Doc. Ratio (Recall Rate) vs. Num. of
  messages
• BFS (Gnutella Message Flooding) (forward to
  degree nodes).
• Random BFS (randomly forward to degree/2 nodes).
• Intelligent Search Mechanism (forward to
  M(degree/2)-1 highest RelevanceRank nodes 1
  random).
• gtRES Heuristic (forward to degree/2 nodes that
  answered gtRES)

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Experimental Evaluation

• Reducing Query Messages (10x10 Experiment)
• Recall Rate vs. Num. of messages with TTL4
• BFS uses 1050 messages w/ recall rate 100
• RBFS uses 220 (20) msgs w/ recall rate 50
• gtRES uses 400 (38) msgs w/ recall rate 70
• ISM uses 400 (38) msgs w/ recall rate 90
• ISM improves over time since Peer Profiles get
  more knowledge.
• ISM and gtRES start out slow since the use RBFS
• until they populate their routing structures

36
Experimental Evaluation

• Digging Deeper by Increasing the TTL (10x10)
• Recall Rate vs. Num. of messages with TTL5
• BFS uses again 1050 messages w/ recall rate 100
• RBFS uses 450 (43) msgs w/ recall rate 82
• gtRES uses 570(54) msgs w/ recall rate 90
• ISM uses 570 (54) msgs w/ recall rate 99

37
Experimental Evaluation

• Reducing Query Response Time (QRT) (10x10
  Experiment)
• BFSs QRT is in the order of 6 seconds
• RBFS, ISM and gtRES use
• 30-60 of BFS for TTL4
• 60-80 of BFS for TTL5
• BFS unnecessary messages increase the user
  perceived delay
• The Query Response Time as a percentage of BFS

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Experimental Evaluation

• The Discarded Message Problem (DMP)
• A query q is identified by a GUID.
• To avoid cycles a node never forwards a query it
  already forwarded.
• DMP occurs if a node has forwarded q with TTL1
  and then receives again q with TTL2, where
  TTL2gtTTL1
• In our experiments approximately 30 of queries
  were affected by the DMP problem.

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Experimental Evaluation

• Improving Recall Rate over Time (400 Experiment)
• 10x10 Queries Experiment suited well ISM
• In this experiment we perform 400 random queries
• BFS overwhelming message create two major
  outbreaks
• ISM improves over time achieving
• 96 Recall Rate using again 38 of Messages

40
Presentation Outline

• Introduction Motivation.
• Search Techniques for P2P systems
• The Intelligent Search Mechanism
• PeerWare Simulation Infrastructure
• Experimental Evaluation.
• Conclusions Future Work.

41
Conclusions

• Efficient Information Retrieval in P2P networks
  is not feasible with the current Search
  Algorithms.
• We propose an Intelligent Search Mechanism that
  uses local knowledge to improve Information
  Retrieval in P2P.
• We implement PeerWare and evaluate the
  performance of various Search Techniques
• The ISM achieves in some cases 100 recall rate
  while using only 57 of the BFS messaging.

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Future Work

• Probe different Network Topologies such as ASMap
  with PowerLaws.
• Deploy larger PeerWares with more queries.
• Probe different Peer-Profile maintenance
  policies.
• Use Stemming/Stop Words to answer more
  accurately.
• Compare the performance of our method with new
  proposed techniques (random gossiping, random
  walkers, etc).
• 60 of Gnutella belongs to 20 ISPs. How to
  exploit that to provide more efficient query
  routing schemes?

43
Information Retrieval in Peer-to-Peer Systems
Dept. of Computer Science Engineering. _at_
University of California - Riverside

• Demetrios Zeinalipour-Yazti

Thank You!
M.Sc. Thesis Defense Monday, May 5, 2003Surge
349 1200-100 PM
Thesis Committee Dr. Dimitrios Gunopulos,
Chairperson Dr. Vana Kalogeraki Dr. Chinya V.
Ravishankar
http//www.cs.ucr.edu/csyiazti/msc.html