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Efficient Search in Peer to Peer Networks. By: Beverly Yang Hector Garcia-Molina Presented By: Anshumaan Rajshiva Date: May 20,2002. P2P Networks. Distributed systems in which nodes of equal roles and capabilities exchange information and services directly with each other. Key Challenges. - PowerPoint PPT Presentation
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Efficient Search in Peer to Efficient Search in Peer to Peer NetworksPeer Networks
By: Beverly Yang
Hector Garcia-Molina
Presented By:Anshumaan RajshivaDate: May 20,2002
P2P NetworksP2P Networks
Distributed systems in which nodes of equal roles and capabilities exchange information and services directly with each other.
Key ChallengesKey ChallengesEfficient techniques for search and retrieval
of data.Best search techniques for a system
depends on the needs of the application.Current search techniques in “loose” P2P
systems tend to be very inefficient, either generating too much load on the system, or providing for a very bad user experience.
Current Reason for Current Reason for Inefficiency Inefficiency
Queries are processed by more nodes than desired.
Suggested ImprovementSuggested Improvement
Processing queries through fewer nodes.
TechniquesTechniques
Iterative DeepeningDirected BFSLocal Indices
Problem FrameworkProblem Framework
P2P: Undirected graph Vertices: nodes in the n/w
Edges: Open connections between neighbors.
Message will travel from A to B in hops. Length of the path: Number of hopsSource of query: Node submitting the query
Problem FrameworkProblem Framework
When a node receives a query it should process the query locally and respond to the query/forward/drop the query
Address of the source node will be unknown to the responding node (scheme used by Gnutella)
Metrics Metrics
Cost: Average Aggregate Bandwidth Average Aggregate Processing Cost
Quality of Results: Number of Results Satisfaction of the
query
CostCost
Message propagates across nodes,each node spend some processing resources on behalf of the query
Main cost described in terms of bandwidth and processing cost
CostCost
Average Aggregate Bandwidth: The average over a set of representative queries of the aggregate BW consumed(in bytes) over each edge on behalf of the query
Average Aggregate Processing Cost: The average over a set of representative queries of the aggregate processing power consumed at each node on behalf of the query
Quality of ResultsQuality of Results
Results from the perspective of userNumber of results: the size of total result setSatisfaction of the query:a query is satisfied
if Z or more results are returned, where Z is some value specified by user
Time to satisfaction: how long the user must wait for the Zth result to arrive
Current TechniquesCurrent Techniques Gnutella: BFS technique is used with depth limit
of D, where D= TTL of the message.At all levels <D query is processed by each node and results are sent to source and at level D query is dropped.
Freenet: uses DFS with depth limit D.Each node forwards the query to a single neighbor and waits for a definite response from the neighbor before forwarding the query to another neighbor(if the query was not satisfied), or forwarding the results back to the query source(if query was satisfied).
Stop and Think…Stop and Think…
Quality of results measured only by number of results then BFS is ideal
If Satisfaction is metrics of choice BFS wastes much bandwidth and processing power
With DFS each node processes the query sequentially,searches can be terminated as soon as the query is satisfied, thereby minimizing cost.But poor response time due to the above
Broadcast PolicyBroadcast Policy
BFS and DFS falls on opposite extremes of bandwidth/processing cost and response time.
Need to find some middle ground between the two extremes, while maintaining quality of results.
Iterative DeepeningIterative Deepening
When satisfaction is the metric of choiceMultiple BFS are initiated with successively
larger depths, until query is satisfied or the maximum depth limit D is reached
Iterative DeepeningIterative Deepening
System wide policy specifying at what depth the iterations are to occur
Last depth in policy must be set to DA waiting period W ( time between
successive iterations in the policy)must be specified
Working of Iterative Working of Iterative DeepeningDeepening
Policy P{a,b,c} S initiates a BFS of depth a by sending out a query
message with TTL=a to all its neighbors Once a node at depth a receives and process the
message, instead of dropping it, the node will store the message temporarily
Query becomes frozen there at all nodes a hops away from S (Frontier nodes)
S receives response from those nodes that have processed the query so far.
Working of Iterative Working of Iterative DeepeningDeepening
After waiting for time W if S finds that the query has already been satisfied, then it does nothing.
Otherwise, if the query is not yet satisfied,s will start the next iteration, initiating BFS at depth b.
S send out a resend message with TTL=a. A node that receives a resend message,simply
unfreeze the query(stored temporarily) and forward the same with TTL= b-a to its neighbors.
This process continues in the similar fashion till TTL=D is reached.At depth D, the query is dropped
Working of Iterative Working of Iterative DeepeningDeepening
To identify queries with Resend messages, every query is assigned a system wide “unique identifier”.
The resend message will contain the identifier of the query it is representing and nodes at the frontier of a search will know which query to unfreeze by inspecting this identifier.
Iterative DeepeningIterative Deepening
Source
Node1Node2 Level 1
Node3 Node4 Level 2
Directed BFSDirected BFSIf minimizing response time is important
then Directed BFS.Strategy used is to send queries to a subset
of nodes that will return many returns quickly by intelligently selecting those nodes based on some parameters.
For this purpose, a node will maintain statistics on its neighbors
Directed BFSDirected BFS
These statistics will be based on the number of results that were received through the neighbors for past queries
By sending the queries to small subset of the nodes, the cost incurred will be reduced significantly
The quality of results is not decreased significantly,provided we make neighbor selection intelligently
Local IndicesLocal Indices
A node maintains an index over the data of each node within r hops of itself, where r is a system wide variable called radius
When a node receives a Query message, it can then process the query on behalf of every node within r hops of itself
Collections of many nodes can be searched by processing the query at few nodes, while keeping the cost low
Local IndicesLocal Indices
R should be small.The index will be small- typically of the
order of 50 KB- independent of the size of the network
Working of Local IndicesWorking of Local Indices
Policy specifies at which depth query will be processed
To create and maintain the indices at each node
All nodes at depths not listed in the policy will simply forward the query to the next depth
Maintaining IndicesMaintaining Indices
Joining a new node: sends a join message with TTL=r and all the nodes within r hops update their indices.
Join message contains the metadata about the joining node
When a node receives this join message it, in turn, send join message containing its meta data directly to the new node.New node updates its indices
Maintaining IndicesMaintaining Indices
Node dies: Other nodes update their indices based on the timeouts
Updating the node: When a node updates its collection, his node will send out a small update message with TTL= r, containing the metadata of the affected item.All nodes receiving this message subsequently update their index.
Results for Iterative Results for Iterative DeepeningDeepening
Results for Iterative Results for Iterative DeepeningDeepening
Results for Directed BFSResults for Directed BFS
Results for Directed BFSResults for Directed BFS
Results for Local IndicesResults for Local Indices
ConclusionsConclusions
Compared to current techniques used in existing systems, the discussed techniques greatly reduce the aggregate cost of processing query over the entire system, while maintaining the quality of results
Schemes are simple and practical to implement on the existing systems.
ConclusionsConclusions
ReferencesReferences
Beverly Yang, Hector Garcia Molina Efficient search in peer to peer network, Available at http://dbpubs.stanford.edu/pub/2001-47