Improve search in unstructured P2P overlay

1

• Improve search in unstructured P2P overlay

2
Peer-to-peer Networks

• Peers are connected by an overlay network.
• Users cooperate to share files (e.g., music,
  videos, etc.)

3
(Search in) Basic P2P Architectures

• Centralized central directory server. (Napster)
• Decentralized search is performed by probing
  peers
• Structured (DHTs) (Can, Chord,) location is
  coupled with topology - search is routed by the
  query.
• Only exact-match queries, tightly controlled
  overlay.
• Unstructured (Gnutella) search is blind -
  probed peers are unrelated to query.
  

4
Topics

• Search strategies
• Beverly Yang and Hector Garcia-Molina, Improving
  Search in Peer-to-Peer Networks, ICDCS 2002
• Arturo Crespo, Hector Garcia-Molina, Routing
  Indices For Peer-to-Peer Systems, ICDCS 2002
• Short cuts
• Kunwadee Sripanidkulchai, Bruce Maggs and Hui
  Zhang, Efficient Content Location Using
  Interest-based Locality in Peer-to-Peer Systems,
  infocom 2003.
• Replication
• Edith Cohen and Scott Shenker, Replication
  Strategies in Unstructured Peer-to-Peer
  Networks, SIGCOMM 2002.

5
Improving Search in Peer-to-Peer Networks

• ICDCS 2002
• Beverly Yang
• Hector Garcia-Molina

6
Motivation

• The propose of a data-sharing P2P system is to
  accept queries from users, and locate and return
  data (or pointers to the data).
• Metrics
• Cost
• Average aggregate bandwidth
• Average aggregate processing cost
• Quality of results
• Number of results
• Satisfaction a query is satisfied if Z (a value
  specified by user) or more results are returned.
• Time to satisfaction

7
Current Techniques

• Gnutella
• BFS with depth limit D.
• Waste bandwidth and processing resources
• Freenet
• DFS with depth limit D.
• Poor response time.

8
Broadcast policies

• Iterative deepening
• Directed BFS
• Local Indices

9
Iterative Deepening

• In system where satisfaction is the metric of
  choice, iterative deepening is a good technique
• Under policy P a, b, c waiting time W
• A source node S first initiates a BFS of depth a
• The query is processed and then becomes frozen at
  all nodes that are a hops from the source
• S waiting for a time period W

10
Iterative Deepening

• If query is not satisfied, S will start the next
  iteration, initiating a BFS of depth b.
• S send a Resend with a TTL of a
• A node that receives a Resend message will simply
  forward the message or if the node is at depth a,
  it will drop the resend message and unfreeze the
  corresponding query by forwarding the query
  message with a TTL of b-a to all its neighbors
• A node need only freeze a query for slightly more
  than W time units before deleting it

11
Directed BFS

• If minimizing response time is important to an
  application, iterative deepening may not be
  appropriate
• A source send query messages to just a subset of
  its neighbors
• A node maintains simple statistics on its
  neighbors
• Number of results received from each neighbor
• Latency of connection

12
Directed BFS (cont)

• Candidate nodes
• Returned the Highest number of results
• The neighbor that returns response messages that
  have taken the lowest average number of hops
• High message count

13
Local Indices

• Each node n maintains an index over the data of
  all nodes within r hops radius.
• All nodes at depths not listed in the policy
  simply forward the query.
• Example policy P 1, 5

14
Experimental result
15
Routing Indices For Peer-to-Peer Systems
Arturo Crespo, Hector Garcia-Molina
Stanford University
crespo,hector_at_db.Stanford.edu
16
Motivation

• A key part of a P2P system is document discovery
• The goal is to help users find documents with
  content of interest across potential P2P sources
  efficiently
• The mechanisms for searching can be classified in
  three categories
• Mechanisms without an index
• Mechanisms with specialized index nodes
  (centralized search)
• Mechanisms with indices at each node (distributed
  search)

17
Motivation (cont.)

• Gnutella uses a mechanism where nodes do not have
  an index
• Queries are propagated from node to node until
  matching documents are found
• Although this approach is simple and robust, it
  has the disadvantage of the enormous cost of
  flooding the network every time a query is
  generated
• Centralized-search systems use specialized nodes
  that maintain an index of the documents available
  in the P2P system like Napster
• The user queries an index node to identify nodes
  having documents with the content
• A centralized system is vulnerable to attack and
  it is difficult to keep the indices up-to-date

18
Motivation (cont.)

• A distributed-index mechanism
• Routing Indices (RIs)
• Give a direction towards the document, rather
  than its actual location
• By using routes the index size is proportional
  to the number of neighbors

19
Peer-to-peer Systems

• A P2P system is formed by a large number of nodes
  that can join or leave the system at any time
• Each node has a local document database that can
  be accessed through a local index
• The local index receives content queries and
  returns pointers to the documents with the
  requested content

20
Query Processing in a Distributed Search P2P
System

• In a distributed-search P2P system, users submit
  queries to any node along with a stop condition
• A node receiving a query first evaluates the
  query against its own database, returns to the
  user pointers to any results
• If the stop condition has not been reached, the
  node selects one or more of its neighbors and
  forwards the query to them
• Queries can be forwarded to the best neighbors in
  parallel or sequentially
• A parallel approach yields better response time,
  but generates higher traffic and may waste
  resources

21
Routing indices

• The objective of a Routing Index (RI) is to allow
  a node to select the best neighbors to send a
  query
• A RI is a data structure that, given a query,
  returns a list of neighbors, ranked according to
  their goodness for the query
• Each node has a local index for quickly finding
  local documents when a query is received. Nodes
  also have a CRI containing
• the number of documents along each path
• the number of documents on each topic

22
Routing indices (cont.)

• Thus, the number of results in a path can be
  estimated
• as
• CRI(si) is the value for the cell at the column
  for topic si and at the row for a neighbor
• The goodness of B 6
• C
  0
• D
  75
• Note that these numbers are just estimates and
  they are subject to overcounts and/or undercounts
• A limitation of using CRIs is that they do not
  take into account the difference in cost due to
  the number of hops necessary to reach a document

23
Using Routing Indices
24
Using Routing Indices (cont.)

• The storage space required by an RI in a node is
  modest as we are only storing index information
  for each neighbor
• t is the counter size in bytes, c is the number
  of categories, N the number of nodes, and b the
  branching factor
• Centralized index would require t (c 1) N
  bytes
• the total for the entire distributed system is t
  (c 1) b N bytes
• the RIs require more storage space overall than a
  centralized index, the cost of the storage space
  is shared among the network nodes

25
Creating Routing Indices
26
Maintaining Routing Indices

• Maintaining RIs is identical to the process used
  for creating them
• For efficiency, we may delay exporting an update
  for a short time so we can batch several updates,
  thus, trading RI freshness for a reduced update
  cost
• We can also choose sending minor updates, but
  reduce accuracy of the RI

27
Hop-count Routing Indices
28
Hop-count Routing Indices (cont.)

• The estimator of a hop-count RI needs a cost
  model to compute the goodness of a neighbor
• We assumes that document results are uniformly
  distributed across the network and that the
  network is a regular tree with fanout F
• We define the goodness (goodness hc) of Neighbor
  i with respect to query Q for hop-count RI as
• If we assume F 3, the goodness of X for a query
  about DB documents would be 1310/3 16.33 and
  for Y would be 031/3 10.33

29
Exponentially aggregated RI

• Each entry of the ERI for node N contains a value
  computed as
• th is the height and F the fanout of the assumed
  regular tree, goodness() is the Compound RI
  estimator , Nj is the summary of the local
  index of neighbor j of N, and T is the topic of
  interest of the entry
• Problems?!

30
Exponentially aggregated RI (cont.)
31
Cycles in the P2P Network

• There are three general approaches for dealing
  with cycles
• No-op solution No changes are made to the
  algorithms
• Cycle avoidance solution In this solution we do
  not allow nodes to create an update connection
  to other nodes if such connection would create a
  cycle
• Absence of global information
• Cycle detection and recovery This solution
  detects cycles sometime after they are formed
  and, after that, takes recovery actions to
  eliminate the effect of the cycles

32
Experimental Results

• Modeling search mechanisms in a P2P system
• We consider three kinds of network topologies
• a tree because it does not have cycles
• we start with a tree and we add extra vertices at
  random (creating cycles)
• a power-law graph, is considered a good model for
  P2P systems and allows us to test our algorithms
  against a realistic topology
• We model the location of document results using
  two distributions uniform and an 80/20 biased
  distribution
• 80/20 assigns uniformly 80 of the document
  results to 20 of the nodes
• In this paper we focus on the network and we use
  the number of messages generated by each
  algorithm as a measure of cost

33
Experimental Results (cont.)
34
Experimental Results (cont.)

• In particular, CRI uses all nodes in the network,
  HRI uses nodes within a predefined a horizon, and
  ERI uses nodes until the exponentially decayed
  value of an index entry reaches a minimum value
• In the case of the No-RI approach, an 80/20
  document distribution penalizes performance as
  the search mechanism needs to visit a number of
  nodes until it finds a content-loaded node

35
Experimental Results (cont.)

• RIs perform better in a power-law network than in
  a tree network (Query)
• In a power-law network a few nodes have a
  significantly higher connectivity than the rest
• Power-law distributions generate network
  topologies where the average path length between
  two nodes is lower than in tree topologies

36
Experimental Results (cont.)

• The tradeoff between query and update costs for
  RIs
• The cost of CRI is much higher when compared with
  HRI and ERI
• ERI only propagate the update to a subset of the
  network

37
Conclusions

• Achieve greater efficiency by placing Routing
  Indices in each node. Three possible RIs
  compound RIs, hopcount RIs, and exponential RIs
• From experiments, ERIs and HRI offer significant
  improvements versus not using an RI, while
  keeping update costs low

38
Efficient Content Location Using Interest-based
Locality in Peer-to-Peer Systems
39
Background

• Each peer is connected randomly, and searching is
  done by flooding.
• Allow keyword search

Example of searching a mp3 file in Gnutella
network. The query is flooded across the network.
40
Background

• DHT (Chord)
• Given a key, Chord will map the key to the node.
• Each node need to maintain O(log N) information
• Each query use O(log N) messages.
• Key search means searching by exact name

41
Interest-based Locality

• Peers have similar interest will share similar
  contents

42
Architecture

• Shortcuts are modular.
• Shortcuts are performance enhancement hints.

43
Creation of shortcuts

• The peer use the underlying topology (e.g.
  Gnutella) for the first few searches.
• One of the return peers is selected from random
  and added to the shortcut lists.
• Each shortcut will be ordered by the metric, e.g.
  success rate, path latency.
• Subsequent queries go through the shortcut lists
  first.
• If fail, lookup through underlying topology.

44
Performance Evaluation

• Performance metric
• success rate
• load characteristics (query packets per peers
  process in the system)
• query scope (the fraction of peers in each query)
• minimum reply path length
• additional state kept in each node

45
Methodology query workload

• Create traffic trace from the real application
  traffic
• Boeing firewall proxies
• Microsoft firewall proxies
• Passively collect the web traffic between CMU and
  the Internet
• Passively collect typical P2P traffic (Kazza,
  Gnutella)
• Use exact matching rather than keyword matching
  in the simulation.
• song.mp3 and my artist song.mp3 will be
  treated as different.

46
Methodology Underlying peers topology

• Based on the Gnutella connectivity graph in 2001,
  with 95 nodes about 7 hops away.
• Searching TTL is set to 7.
• For each kind of traffic (Boeing, Microsoft
  etc), run 8 times simulations, each with 1 hour.

47
Simulation Results success rate
48
Simulation Results load and path length
-- Query load for Boeing and Microsoft Traffic
-- Average path length of the traces
49
Increase Number of Shortcuts
Enhancement of Interest-based Locality
50
Using Shortcuts Shortcuts
Enhancement of Interest-based Locality

• Idea

Add the shortcuts shortcut
Performance gain of 7 on average
51
Interest-based Structures

• When viewed as an undirected graph
• In the first 10 minutes, there are many connected
  components, each component has a few peers in
  between.
• At the end of simulation, there are few connected
  components, each component has several hundred
  peers. Each component is well connected.
• The clustering coefficient is about 0.6 0.7,
  which is higher than that in Web graph.

52
Sensitivity of Shortcuts

• Run Interest based shortcuts over DHT (Chord)
  instead of Gnutella.

Query load is reduced by a factor 2 4. Query
scope is reduced from 7/N to 1.5/N
53
Conclusion

• Interest based shortcuts are modular and
  performance enhancement hints over existing P2P
  topology.
• Shortcuts are proven can enhance the searching
  efficiencies.
• Shortcuts form clusters within a P2P topology,
  and the clusters are well connected.

54
Replication Strategies in Unstructured
Peer-to-Peer Networks

• Edith Cohen
• ATT Labs-research

Scott Shenker ICIR
55
(replication in) P2P architectures

• No proactive replication (Gnutella)
• Hosts store and serve only what they requested
• A copy can be found only by probing a host with a
  copy

56
Question how to use replication to improve
search efficiency in unstructured networks with a
proactive replication mechanism ?
57
Search and replication model

Unstructured networks with replication of keys or
copies. Peers probed (in the search and
replication process) are unrelated to query/item

• Search probe hosts, uniformly at random, until
  the query is satisfied (or the search max size is
  exceeded)

• Replication Each host can store up to r copies
  of items.

Goal minimize average search size (number of
probes till query is satisfied)
58
Search size

• What is the search size of a query ?
• Soluble queries number of probes until answer is
  found.
• We look at the Expected Search Size (ESS) of
  each item. The ESS is inversely proportional to
  the fraction of peers with a copy of the item.

59
Search Example

• 2 probes

4 probes
60
Expected Search Size (ESS)

• m items with relative query rates
• q1 gt q2 gt q3 gt gt qm. Si qi 1

• n nodes, capacity r, Rn r
• ri number of copies of the ith items
• Allocation p1(r1/R), p2, p3,, pm Si pi
  1
• ith item is allocated pi fraction of
  storage.

• Search size for ith item is a Geometric r.v. with
  mean Ai 1/(r pi ).
• ESS is Si qi Ai (Si qi / pi)/r

61
Uniform and Proportional Replication

• Two natural strategies
• Uniform Allocation pi 1/m
• Simple, resources are divided equally
• Proportional Allocation pi qi
• Fair, resources per item proportional to demand
• Reflects current P2P practices

62
Basic Questions

• How do Uniform and Proportional allocations
  perform/compare ?
• Which strategy minimizes the Expected Search Size
  (ESS) ?
• Is there a simple protocol that achieves optimal
  replication in decentralized unstructured
  networks ?

63
ESS under Uniform and Proportional Allocations
(soluble queries)

• Lemma The ESS under either Uniform or
  Proportional allocations is m/r
• Independent of query rates (!!!)
• Same ESS for Proportional and Uniform (!!!)

• Proof

Proportional ASS is (Si qi / pi)/r (Si qi /
qi)/r m/r
Uniform ASS is (Si qi / pi)/r (Si m qi)/r
(m/r) Si qi m/r pi(R/m)/R
64
Space of Possible Allocations

• Definition Allocation p1, p2, p3,, pm is
  in-between Uniform and Proportional if
  for 1lt i ltm, q
  i1/q i lt p i1/p i lt 1
• Theorem1 All (strictly) in-between strategies
  are (strictly) better than Uniform and
  Proportional

Theorem2 p is worse than Uniform/Proportional if
for all i, p i1/p i gt 1 (more popular gets
less) OR for all i, q i1/q i gt p i1/p i (less
popular gets less than fair share)
65
So, what is the best strategy for soluble queries
?
66
Square-Root Allocation

• pi is proportional to square-root(qi)

• Lies In-between Uniform and Proportional
• Theorem Square-Root allocation minimizes the ESS
  (on soluble queries)
• Minimize Si qi / pi such that Si pi 1

67
How much can we gain by using SR ?
Zipf-like query rates
68
Replication Algorithms

• Uniform and Proportional are easy -
• Uniform When item is created, replicate its key
  in a fixed number of hosts.
• Proportional for each query, replicate the key
  in a fixed number of hosts

Desired properties of algorithm

• Fully distributed where peers communicate through
  random probes minimal bookkeeping and no more
  communication than what is needed for search.
• Converge to/obtain SR allocation when query rates
  remain steady.

69
Model for Copy Creation/Deletion

• Creation after a successful search, C(s) new
  copies are created at random hosts.
• Deletion is independent of the identity of the
  item copy survival chances are non-decreasing
  with creation time. (i.e., FIFO at each node)

70
Creation/Deletion Process
Corollary
then

• If

71
SR Replication Algorithms

• Path replication number of new copies C(s) is
  proportional to the size of the search
• Probe memory each peer records number and
  combined search size of probes it sees for each
  item. C(S) is determined by collecting this info
  from number of peers proportional to search size.
  
• Extra communication (proportional to that needed
  for search).

72
Path Replication

• Number of new copies produced per query, ltCigt, is
  proportional to search size 1/pi
• Creation rate is proportional to qi ltCigt
• Steady state creation rate proportional to
  allocation pi, thus

73
Summary

• Random Search/replication Model probes to
  random hosts
• Soluble queries
• Proportional and Uniform allocations are two
  extremes with same average performance
• Square-Root allocation minimizes Average Search
  Size
• OPT (all queries) lies between SR and Uniform
• SR/OPT allocation can be realized by simple
  algorithms.