1. Peer-to-Peer Systems Chapter 25

2. What is Peer-to-Peer (P2P)? Napster? Gnutella? Most people think of P2P as music sharing

3. What is a peer? Contrasted with Client-Server model Servers are centrally maintained and administered Client has fewer resources than a server

4. What is a peer? A peer’s resources are similar to the resources of the other participants P2P – peers communicating directly with other peers and sharing resources

5. P2P Concepts Client-client as opposed to client-server File sharing: I get a copy from someone, and now make it available for others to download---copies are/workload is spread out Advantages: Scalable, stable, self-repairing Process: A peer joins the system when a user starts the application, contributes some resources while making use of the resources provided by others, and leaves the system when the user exits the application. Session: One such join-participate-leave cycle Churn: The independent arrival and departure by thousands—or millions— of peers creates the collective effect we call churn. The user-driven dynamics of peer participation must be taken into account in both the design and evaluation of any P2P application. For example, the distribution of session length can affect the overlay structure, the resiliency of the overlay, and the selection of key design parameters.

6. Types of clients Based on the client behavior, there are three types of clients: True clients (not active participants; take but don’t give; short duration of stay) Peers: Clients that stay long enough and well-connected enough to participate actively (Take and give) Servers (Give, but don’t take) Safe vs. probabilistic protocols Mostly logarithmic order of performance/cost

7. 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

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 Sharing Benefits Cost sharing Resource aggregation Improved scalability/reliability Anonymity/privacy Dynamism

11. P2P Application Taxonomy P2P Systems Distributed Computing SETI@home File Sharing Gnutella Collaboration Jabber Platforms JXTA

12. Management/Placement Challenges Per-node state Bandwidth usage Search time Fault tolerance/resiliency

13. Approaches Centralized Flooding Document Routing

14. Centralized Napster model Benefits: –Efficient search –Limited bandwidth usage –No per-node state Drawbacks: –Central point of failure –Limited scale BobAlice JaneJudy

15. Flooding Gnutella model Benefits: –No central point of failure –Limited per-node state Drawbacks: –Slow searches –Bandwidth intensive Bob Alice Jane Judy Carl

16. Connectivity

17. Napster Uses a centralized directory mechanism –To control the selection of peers –To generate other revenue-generating activities In addition is has several regional servers Users first connect to the Napster’s centralized server to one of the regional servers Basically, each client system has a Napster proxy that keeps track of the local shared files and informs the regional server Napster uses some heuristic evaluation mechanisms about the reliability of a client before it starts using it as a shared workspace

18. Gnutella and Kazaa Unlike Napster, it is a pure P2P with no centralized component---all peers are completely equal Protocol: –Ensures that each user system is concerned with a few Gnutella nodes –Search for files: if the distance specified is 4, then all machines within 4 hops of the client will be probed (1 st all M/C within 1 hop; then 2 hops; and so on) –The anycast mechanism becomes extremely costly as system scales up. Kaaza also does not have a centralized control (as Gnutella); it uses Plaxton trees.

19. CAN Content Addressable Network Each object is expected to have a unique system wide name or identifier The name is hashed into a d-tuple--- identifier is converted into a random- looking number using some cryptographic hash function In a 2-dimensional CAN the id is hashed to a 2-dimensional tuple: (x,y) Same scheme is used to convert machine IDs Recursively subdivide the space of possible d-dimensional identifiers, storing each object at the node owning the part of the space (zone) that object’s ID falls in. When a new node is added, it shares its space with the new node; similarly when a node leaves, its space is owned by a nearby node Once a user provides the search key, it is converted to (x,y); the receiving CAN node finds a path from itslef to the node having (x,y) space. If d is the dimensions, and N is the #of nodes, then the number of hops is (d/4)*N 1/d TO take care of node failures, there will be backups. Cost is high when there are frequent joins/leaves

20. Document Routing FreeNet, Chord, CAN, Tapestry, Pastry model Benefits: –More efficient searching –Limited per-node state Drawbacks: –Limited fault-tolerance vs redundancy 001 012 212 305 332 212 ?

21. 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*n 1/d steps, where n is the total number of nodes Slide modified from another presentation

22. CAN Example: Two Dimensional Space Space divided between nodes All nodes cover the entire space Each node covers either a square or a rectangular area of ratios 1:2 or 2:1 Example: –Node n1:(1, 2) first node that joins  cover the entire space 1 234 5 670 1 2 3 4 5 6 7 0 n1 Slide modified from another presentation

23. CAN Example: Two Dimensional Space Node n2:(4, 2) joins  space is divided between n1 and n2 1 234 5 670 1 2 3 4 5 6 7 0 n1 n2 Slide modified from another presentation

24. CAN Example: Two Dimensional Space Node n2:(4, 2) joins  space is divided between n1 and n2 1 234 5 670 1 2 3 4 5 6 7 0 n1 n2 n3 Slide modified from another presentation

25. CAN Example: Two Dimensional Space Nodes n4:(5, 5) and n5:(6,6) join 1 234 5 670 1 2 3 4 5 6 7 0 n1 n2 n3 n4 n5 Slide modified from another presentation

26. CAN Example: Two Dimensional Space Nodes: n1:(1, 2); n2:(4,2); n3:(3, 5); n4:(5,5);n5:(6,6) Items: f1:(2,3); f2:(5,1); f3:(2,1); f4:(7,5); 1 234 5 670 1 2 3 4 5 6 7 0 n1 n2 n3 n4 n5 f1 f2 f3 f4 Slide modified from another presentation

27. CAN Example: Two Dimensional Space Each item is stored by the node who owns its mapping in the space 1 234 5 670 1 2 3 4 5 6 7 0 n1 n2 n3 n4 n5 f1 f2 f3 f4 Slide modified from another presentation

28. CAN: Query Example Each node knows its neighbors in the d-space Forward query to the neighbor that is closest to the query id Example: assume n1 queries f4 Can route around some failures –some failures require local flooding 1 234 5 670 1 2 3 4 5 6 7 0 n1 n2 n3 n4 n5 f1 f2 f3 f4 Slide modified from another presentation

29. CAN: Query Example Each node knows its neighbors in the d-space Forward query to the neighbor that is closest to the query id Example: assume n1 queries f4 Can route around some failures –some failures require local flooding 1 234 5 670 1 2 3 4 5 6 7 0 n1 n2 n3 n4 n5 f1 f2 f3 f4 Slide modified from another presentation

30. CAN: Query Example Each node knows its neighbors in the d-space Forward query to the neighbor that is closest to the query id Example: assume n1 queries f4 Can route around some failures –some failures require local flooding 1 234 5 670 1 2 3 4 5 6 7 0 n1 n2 n3 n4 n5 f1 f2 f3 f4 Slide modified from another presentation

31. CAN: Query Example Each node knows its neighbors in the d-space Forward query to the neighbor that is closest to the query id Example: assume n1 queries f4 Can route around some failures –some failures require local flooding 1 234 5 670 1 2 3 4 5 6 7 0 n1 n2 n3 n4 n5 f1 f2 f3 f4 Slide modified from another presentation

32. CFS and PAST Files are replicated prior to storage--- copies are stored at adjacent locations in the hashed-id space Make use of indexing systems to locate nodes on which they store objects or from which they retrieve copies IDs are hashed to a 1-dimensional space Leaves/Joins result in several file copies--- could be a bottleneck

33. OceanStore Focused on long term archival storage (rather than file sharing)---e.g., digital libraries Ensure codes --- class of error-correcting codes that can reconstruct a valid copy of a file given some percentage of copies

34. Distributed Indexing in P2P Two requirements: –A lookup mechanism to track down a node holding an object –A superimposed file system that knows how to store and retrieve files DNS---a distributed object locator: M/C names to IP addresses P2P indexing tools let users store (key, value) pairs---a distributed hash system

35. Chord It is a major DHT architecture Forms a massive virtual ring in which every node in the distributed system is a member---each owning part of a periphery. If hash value of a node is h, and the lower value is hL, and the higher is hH, then the node with h owns objects in the range: hL< k <= h E.g., if a,b, c hash to 100, 120, and 175, respectively, then b is responsible for IDs in the range 101-120; c is responsible for 121- 175. When a new node joins, it computes its hash and then joins at the right place in the ring; then the corresponding range of objects are transferred to it. Potential problems---adjacent nodes could be far apart in distance Statistics: Average path length in an internet is 22 network routers leading an average length of 10 milliseconds; this further slowed by slow nodes

36. Chord---cont. Two mechanisms in Chord: –Applications that repeatedly access the same object---Chord nodes cache link information so that after the initial lookup each node on the path remembers (its IP addresses) all nodes on the path for future use. –When a node joins the Chord system, at hashed location hash(key), it looks up the nodes associated with hash(key)/2, hash(key)/4, hash(key)/8, etc. This is in a circular range. –It uses a binary search to locate an object resulting in log(N) search time; but this is not good enough---cached pointers help the effort –Frequent leaves creates dangling pointers—a problem –Churn—frequent joins/leaves---results in several key shuffles---a problem

37. Document Routing – Chord MIT project Uni-dimensional ID space Keep track of log N nodes Search through log N nodes to find desired key N32 N10 N5 N20 N110 N99 N80 N60 K19

38. Pastry Basic idea: Construction of a matrix (of size r x log r N) of pointers at each participating node---r is a radix and N is the size of the network; If N = 16 5 and r =5, then each matrix is of size 16 x 5. Maps keys to a hashed space (like others) By following the pointers, a request is routed closer and closer to the node owning the portion of the space that an object belongs to. Hexadecimal addresses: with r=5, the address has 5 hexadecimals: 65A1FC as in the example. Top row has: indices from 0 to F representing the 1 st hexadecimal in the hash address. For 65A1FC, there is a match at 6, so it has another level of index: 0-F representing the 2 nd position in the address. For the current node, there is a 2 nd level match at 5; so this node is extended to next level from 0-F: once again there is a match at A which is further expanded to the 4 th level; This has 0-F in the 4 th position, current one matching at F. This is further expanded to 5 th level from 0-F (not shown in Figure 25.5). Thus, it has 16 x 5 matrix of pointers to nodes. To take care of joins/leaves, Pastry periodically probes each pointer (finger) and repairs broken links when it notices problems It uses an application-level multicast (overlay multicast architecture)

39. Doc Routing – Tapestry/Pastry Global mesh Suffix-based routing Uses underlying network distance in constructing mesh 13FE ABFE 1290 239E 73FE 9990 F990 993E 04FE 43FE

40. 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

41. Comparing Guarantees log b NNeighbor map Pastry b log b N log b NGlobal Mesh Tapestry 2ddN 1/d Multi- dimensional CAN log N Uni- dimensional Chord StateSearchModel b log b N + b

42. Remaining Problems? Hard to handle highly dynamic environments Usable services Methods don’t consider peer characteristics