Peer to-peer

Peer to-peer

• 1.
  An Overview ofPeer-to-Peer Sami Rollins 11/14/02
• 2.
  Outline • P2P Overview – What is a peer? – Example applications – Benefits of P2P • P2P Content Sharing – Challenges – Group management/data placement approaches – Measurement studies
• 3.
  What is Peer-to-Peer(P2P)? • Napster? • Gnutella? • Most people think of P2P as music sharing
• 4.
  What is apeer? • Contrasted with Client-Server model • Servers are centrally maintained and administered • Client has fewer resources than a server
• 5.
  What is apeer? • A peer’s resources are similar to the resources of the other participants • P2P – peers communicating directly with other peers and sharing resources
• 6.
  Levels of P2P-ness •P2P as a mindset – Slashdot • P2P as a model – Gnutella • P2P as an implementation choice – Application-layer multicast • P2P as an inherent property – Ad-hoc networks
• 7.
  P2P Application Taxonomy P2P Systems Distributed Computing File Sharing Collaboration Platforms SETI@home Gnutella Jabber JXTA
• 8.
  P2P Goals/Benefits • Cost sharing • Resource aggregation • Improved scalability/reliability • Increased autonomy • Anonymity/privacy • Dynamism • Ad-hoc communication
• 9.
  P2P File Sharing •Content exchange – Gnutella • File systems – Oceanstore • Filtering/mining – Opencola
• 10.
  P2P File SharingBenefits • Cost sharing • Resource aggregation • Improved scalability/reliability • Anonymity/privacy • Dynamism
• 11.
  Research Areas • Peer discovery and group management • Data location and placement • Reliable and efficient file exchange • Security/privacy/anonymity/trust
• 12.
  Current Research • Groupmanagement and data placement – Chord, CAN, Tapestry, Pastry • Anonymity – Publius • Performance studies – Gnutella measurement study
• 13.
  Management/Placement Challenges • Per-node state • Bandwidth usage • Search time • Fault tolerance/resiliency
• 14.
  Approaches • Centralized • Flooding •Document Routing
• 15.
  Centralized Bob Alice • Napster model • Benefits: – Efficient search – Limited bandwidth usage – No per-node state • Drawbacks: – Central point of failure Judy Jane – Limited scale
• 16.
  Flooding Carl Jane • Gnutella model • Benefits: – No central point of failure – Limited per-node state • Drawbacks: Bob – Slow searches – Bandwidth intensive Alice Judy
• 17.
  Document Routing 001 012 • FreeNet, Chord, CAN, Tapestry, Pastry model 212 ? 212 ? • Benefits: 332 – More efficient searching 212 – Limited per-node state 305 • Drawbacks: – Limited fault-tolerance vs redundancy
• 18.
  Document Routing –CAN • Associate to each node and item a unique id in an d-dimensional space • Goals – Scales to hundreds of thousands of nodes – Handles rapid arrival and failure of nodes • Properties – Routing table size O(d) – Guarantees that a file is found in at most d*n1/d steps, where n is the total number of nodes Slide modified from another presentation
• 19.
  CAN Example: Two Dimensional Space • Space divided between nodes 7 • All nodes cover the entire 6 space 5 • Each node covers either a 4 square or a rectangular area of ratios 1:2 or 2:1 3 n1 • Example: 2 – Node n1:(1, 2) first node that 1 joins  cover the entire space 0 0 1 2 3 4 5 6 7 Slide modified from another presentation
• 20.
  CAN Example: Two Dimensional Space • Node n2:(4, 2) joins  space 7 is divided between n1 and n2 6 5 4 3 n1 n2 2 1 0 0 1 2 3 4 5 6 7 Slide modified from another presentation
• 21.
  CAN Example: Two Dimensional Space • Node n2:(4, 2) joins  space 7 is divided between n1 and n2 6 n3 5 4 3 n1 n2 2 1 0 0 1 2 3 4 5 6 7 Slide modified from another presentation
• 22.
  CAN Example: Two Dimensional Space • Nodes n4:(5, 5) and n5:(6,6) 7 join 6 n5 n3 n4 5 4 3 n1 n2 2 1 0 0 1 2 3 4 5 6 7 Slide modified from another presentation
• 23.
  CAN Example: Two Dimensional Space • Nodes: n1:(1, 2); n2:(4,2); n3: 7 (3, 5); n4:(5,5);n5:(6,6) 6 n5 • Items: f1:(2,3); f2:(5,1); f3: n3 n4 5 f4 (2,1); f4:(7,5); 4 f1 3 n1 n2 2 f3 1 0 f2 0 1 2 3 4 5 6 7 Slide modified from another presentation
• 24.
  CAN Example: Two Dimensional Space • Each item is stored by the 7 node who owns its mapping in the space 6 n3 n4 n5 5 f4 4 f1 3 n1 n2 2 f3 1 0 f2 0 1 2 3 4 5 6 7 Slide modified from another presentation
• 25.
  CAN: Query Example •Each node knows its neighbors in the d-space 7 • Forward query to the 6 n5 n4 neighbor that is closest to the 5 n3 f4 query id 4 • Example: assume n1 queries 3 f1 f4 n1 n2 2 • Can route around some f3 1 failures f2 0 – some failures require local 0 1 2 3 4 5 6 7 flooding Slide modified from another presentation
• 26.
  CAN: Query Example •Each node knows its neighbors in the d-space 7 • Forward query to the 6 n5 n4 neighbor that is closest to the 5 n3 f4 query id 4 • Example: assume n1 queries 3 f1 f4 n1 n2 2 • Can route around some f3 1 failures f2 0 – some failures require local 0 1 2 3 4 5 6 7 flooding Slide modified from another presentation
• 27.
  CAN: Query Example •Each node knows its neighbors in the d-space 7 • Forward query to the 6 n5 n4 neighbor that is closest to the 5 n3 f4 query id 4 • Example: assume n1 queries 3 f1 f4 n1 n2 2 • Can route around some f3 1 failures f2 0 – some failures require local 0 1 2 3 4 5 6 7 flooding Slide modified from another presentation
• 28.
  CAN: Query Example •Each node knows its neighbors in the d-space 7 • Forward query to the 6 n5 n4 neighbor that is closest to the 5 n3 f4 query id 4 • Example: assume n1 queries 3 f1 f4 n1 n2 2 • Can route around some f3 1 failures f2 0 – some failures require local 0 1 2 3 4 5 6 7 flooding Slide modified from another presentation
• 29.
  Node Failure Recovery •Simple failures – know your neighbor’s neighbors – when a node fails, one of its neighbors takes over its zone • More complex failure modes – simultaneous failure of multiple adjacent nodes – scoped flooding to discover neighbors – hopefully, a rare event Slide modified from another presentation
• 30.
  Document Routing –Chord N5 N10 N110 K19 • MIT project N20 • Uni-dimensional ID N99 space N32 • Keep track of log N nodes N80 • Search through log N nodes to find desired key N60
• 31.
  Doc Routing –Tapestry/Pastry 43F 993 13F E E E • Global mesh • Suffix-based routing 73F F99 • Uses underlying network E 0 distance in constructing 04F E mesh 999 ABF 0 E 239 E 129 0
• 32.
  Comparing Guarantees Model Search State Chord Uni- log N log N dimensional Multi- CAN dN1/d 2d dimensional Tapestry Global Mesh logbN b logbN Pastry Neighbor logbN b logbN + b map
• 33.
  Remaining Problems? • Hardto handle highly dynamic environments • Usable services • Methods don’t consider peer characteristics
• 34.
  Measurement Studies • “Free Riding on Gnutella” • Most studies focus on Gnutella • Want to determine how users behave • Recommendations for the best way to design systems
• 35.
  Free Riding Results •Who is sharing what? • August 2000 The top Share As percent of whole 333 hosts (1%) 1,142,645 37% 1,667 hosts (5%) 2,182,087 70% 3,334 hosts (10%) 2,692,082 87% 5,000 hosts (15%) 2,928,905 94% 6,667 hosts (20%) 3,037,232 98% 8,333 hosts (25%) 3,082,572 99%
• 36.
  Saroiu et alStudy • How many peers are server-like…client- like? – Bandwidth, latency • Connectivity • Who is sharing what?
• 37.
  Saroiu et alStudy • May 2001 • Napster crawl – query index server and keep track of results – query about returned peers – don’t capture users sharing unpopular content • Gnutella crawl – send out ping messages with large TTL
• 38.
  Results Overview • Lotsof heterogeneity between peers – Systems should consider peer capabilities • Peers lie – Systems must be able to verify reported peer capabilities or measure true capabilities
• 39.
  Measured Bandwidth
• 40.
  Reported Bandwidth
• 41.
  Measured Latency
• 42.
  Measured Uptime
• 43.
  Number of SharedFiles
• 44.
  Connectivity
• 45.
  Points of Discussion •Is it all hype? • Should P2P be a research area? • Do P2P applications/systems have common research questions? • What are the “killer apps” for P2P systems?
• 46.
  Conclusion • P2P isan interesting and useful model • There are lots of technical challenges to be solved