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Indranil Gupta Lecture 10 Peer-to-peer systems – Part II

Computer Science 425 Distributed Systems. Indranil Gupta Lecture 10 Peer-to-peer systems – Part II. DHT=Distributed Hash Table. A hash table allows you to insert, lookup and delete objects with keys A distributed hash table allows you to do the same in a distributed setting (objects=files)

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Indranil Gupta Lecture 10 Peer-to-peer systems – Part II

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  1. Computer Science 425Distributed Systems Indranil Gupta Lecture 10 Peer-to-peer systems – Part II

  2. DHT=Distributed Hash Table • A hash table allows you to insert, lookup and delete objects with keys • A distributed hash table allows you to do the same in a distributed setting (objects=files) • Performance Concerns: • Load balancing • Fault-tolerance • Efficiency of lookups and inserts • Locality • Napster, Gnutella, FastTrack are all DHTs • So is Chord, a structured peer to peer system that we study next

  3. Comparative Performance

  4. Comparative Performance

  5. Chord • Developers: I. Stoica, D. Karger, F. Kaashoek, H. Balakrishnan, R. Morris, Berkeley and MIT • Intelligent choice of neighbors to reduce latency and message cost of routing (lookups/inserts)

  6. Chord • Uses Consistent Hashing on peer’s address • SHA-1 = Simple Hash Algorithm-1, a standard hashing function) • Difficult to reverse • SHA-1(ip_address,port) 160 bit string • Output truncated to m (< 160)bits • Called peer id (integer between 0 and ) • Not unique but id conflicts very very unlikely • “Consistent”? Any node A can calculate the peer id of any other node B, given B’s ip address and port number • Can then map peers to one of logical points on a circle

  7. Ring of peers Say m=7 0 N16 N112 And 6 peers/nodes N96 N32 N45 N80

  8. Peer pointers (1): successors Say m=7 0 N16 N112 N96 N32 N45 N80 (similarly predecessors)

  9. Peer pointers (2): finger tables Say m=7 Finger Table at N80 0 N16 N112 i ft[i] 0 96 1 96 2 96 3 96 4 96 5 112 6 16 80 + 25 80 + 26 N96 N32 80 + 24 80 + 23 80 + 22 80 + 21 80 + 20 N45 N80 ith entry at peer with id n is first peer with id >=

  10. What about the files? • Filenames also mapped using same consistent hash function • SHA-1(filename) 160 bits, truncated to m bits=file id or key • File cnn.com/index.html that maps to file id /key 42 is stored at first peer with id >= 42 • Note that we are considering a different file-sharing application here : cooperative web caching • “Peer”=client browser, “files”=html objects • Peers can now fetch html objects from other peers that have them, rather than from server • The same discussion applies to any other file sharing application, including that of mp3 files

  11. Mapping Files Say m=7 0 N16 N112 N96 N32 N45 N80 File cnn.com/index.html with key K42 stored here

  12. Search Say m=7 0 N16 N112 N96 N32 Who has cnn.com/index.html? (hashes to K42) N45 N80 File cnn.com/index.html with key K42 stored here

  13. Search At node n, send query for key k to largest successor/finger entry < k (all mod m) if none exist, send query to successor(n) Say m=7 0 N16 N112 N96 N32 Who has cnn.com/index.html? (hashes to K42) N45 N80 File cnn.com/index.html with key K42 stored here

  14. Search At node n, send query for key k to largest successor/finger entry < k (all mod m) if none exist, send query to successor(n) Say m=7 0 N16 N112 All “arrows” are RPCs N96 N32 Who has cnn.com/index.html? (hashes to K42) N45 N80 File cnn.com/index.html with key K42 stored here

  15. Here Analysis Next hop Key Search takes O(log(N)) time Proof • (intuition): at each step, distance between query and peer-with-file reduces by a factor of at least 2 (why?) Takes at most m steps: is at most a constant multiplicative factor above N, lookup is O(log(N)) • (intuition): after log(N) forwardings, distance to key is at most (why?) Number of node identifiers in a range of is O(log(N)) with high probability (why? SHA-1!) So using successors in that range will be ok

  16. Analysis (contd.) • O(log(N)) search time holds for file insertions too (in general for routing to any key) • “Routing” can thus be used as a building block for other applications than file sharing [can you name one?] • O(log(N)) time true only if finger and successor entries correct • When might these entries be wrong?

  17. Analysis (contd.) • O(log(N)) search time holds for file insertions too (in general for routing to any key) • “Routing” can thus be used as a building block for other applications than file sharing [can you name one?] • O(log(N)) time true only if finger and successor entries correct • When might these entries be wrong? • When you have failures

  18. Search under peer failures Lookup fails (N16 does not know N45) Say m=7 0 N16 N112 X N96 X N32 Who has cnn.com/index.html? (hashes to K42) X N45 N80 File cnn.com/index.html with key K42 stored here

  19. Search under peer failures One solution: maintain r multiple successor entries in case of failure, use successor entries Say m=7 0 N16 N112 N96 X N32 Who has cnn.com/index.html? (hashes to K42) N45 N80 File cnn.com/index.html with key K42 stored here

  20. Search under peer failures • Choosing r=2log(N) suffices to maintain correctness with high probability • Say 50% of nodes fail • Pr(for given node, at least one successor alive)= • Pr(above is true for all alive nodes)=

  21. Search under peer failures (2) Lookup fails (N45 is dead) Say m=7 0 N16 N112 N96 N32 Who has cnn.com/index.html? (hashes to K42) X X N45 N80 File cnn.com/index.html with key K42 stored here

  22. Search under peer failures (2) One solution: replicate file/key at r successors and predecessors Say m=7 0 N16 N112 N96 N32 Who has cnn.com/index.html? (hashes to K42) K42 replicated X N45 N80 File cnn.com/index.html with key K42 stored here K42 replicated

  23. Need to deal with dynamic changes • Peers fail • New peers join • Peers leave All the time  Need to update successors and fingers, and ensure keys reside in the right places

  24. New peers joining Introducer directs N40 to N32 N32 updates successor to N40 N40 initializes successor to N45, and obtains fingers from it N40 periodically talks to neighbors to update own finger table Say m=7 0 Stabilization protocol N16 N112 N96 N32 N40 N45 N80

  25. New peers joining (2) N40 may need to copy some files/keys from N45 (files with fileid between 32 and 40) Say m=7 0 N16 N112 N96 N32 N40 N45 N80 K34,K38

  26. New peers joining (3) • A new peer affects O(log(N)) other finger entries in the system • Number of messages per peer join= O(log(N)*log(N)) • Similar set of operations for dealing with peers leaving

  27. Stabilization Protocol • Concurrent peer joins, leaves, failures might cause loopiness of pointers, and failure of lookups • Chord peers periodically run a stabilization algorithm that checks and updates pointers and keys • Ensures non-loopiness of fingers, eventual success of lookups and O(log(N)) lookups w.h.p. • [TechReport on Chord webpage] defines weak and strong stability • Each stabilization round at a peer involves a constant number of messages • Strong stability takes stabilization rounds (!)

  28. Experimental Results • Sigcomm 01 paper had results from simulation of a C++ prototype • SOSP 01 paper had more results from a 12-node Internet testbed deployment • We’ll touch briefly on the first set of results • 10,000 peer system

  29. Lookups Average Messages per Lookup log, as expected (X-axis is logarithmic) Number of Nodes

  30. Wrap-up Notes • Memory: O(log(N)) successor pointer, m finger entries • Indirection: store a pointer instead of the actual file • Does not handle partitions of the group (can you suggest a possible solution?)

  31. Wrap-up Notes (2) • When nodes are constantly joining, leaving, failing • Significant effect to consider: traces from the Overnet system show hourly peer turnover rates (churn) could be 10-15% of total number of nodes in system • Leads to excessive (unnecessary) key copying • further, remember that keys are replicated, so all these copies will need to be copied around as nodes join and leave • Stabilization algorithm may need to consume more bandwidth to keep up • There exist alternative DHTs that are churn-resistant

  32. Wrap-up Notes (3) • Current status of project: • Protocol constantly undergoing change • File systems (CFS,Ivy) built on top of Chord • DNS lookup service built on top of Chord • Spawned research on many interesting issues about p2p systems http://www.pdos.lcs.mit.edu/chord/

  33. Summary • Chord protocol • More structured than Gnutella • O(log(N)) memory and lookup cost • Simple lookup algorithm, rest of protocol complicated • Stabilization works, but how far can it go? • Next lecture: The Internet! Reading: Chapter 3

  34. Optional Material

  35. Fault-tolerance 500 nodes (avg. path len=5) Stabilization runs every 30 s 1 joins&fails every 10 s (3 fails/stabilization round) => 6% failed lookups Numbers look fine here, but does stabilization scale with N?

  36. Load Balancing Large variance Solution: Each real node joins as r multiple virtual nodes smaller load variation, lookup cost is O(log(N*r)= O(log(N)+log(r))

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