Peer-to-peer computing research: a fad?

1. Peer-to-peer computing research:a fad? Frans Kaashoek kaashoek@lcs.mit.edu Joint work with: H. Balakrishnan, P. Druschel , J. Hellerstein , D. Karger, R. Karp, J. Kubiatowicz, B. Liskov, D. Mazières, R. Morris, S. Shenker, and I. Stoica

2. What is a P2P system? Node Node Node • A distributed system architecture: • No centralized control • Nodes are symmetric in function • Larger number of unreliable nodes • Enabled by technology improvements Internet Node Node

3. P2P: an exciting social development • Internet users cooperating to share, for example, music files • Napster, Gnutella, Morpheus, KaZaA, etc. • Lots of attention from the popular press “The ultimate form of democracy on the Internet” “The ultimate threat to copy-right protection on the Internet”

4. How to build critical services? • Many critical services use Internet • Hospitals, government agencies, etc. • These services need to be robust • Node and communication failures • Load fluctuations (e.g., flash crowds) • Attacks (including DDoS)

5. The promise of P2P computing • Reliability: no central point of failure • Many replicas • Geographic distribution • High capacity through parallelism: • Many disks • Many network connections • Many CPUs • Automatic configuration • Useful in public and proprietary settings

6. Traditional distributed computing:client/server Server • Successful architecture, and will continue to be so • Tremendous engineering necessary to make server farms scalable and robust Client Client Internet Client Client

7. Application-level overlays Site 2 Site 3 N • One per application • Nodes are decentralized • NOC is centralized N N ISP1 ISP2 Site 1 N N ISP3 N Site 4 P2P systems are overlay networks without central control

8. …. node node node Distributed hash table (DHT) (File sharing) Distributed application data get (key) put(key, data) (DHash) Distributed hash table lookup(key) node IP address (Chord) Lookup service • Application may be distributed over many nodes • DHT distributes data storage over many nodes

9. A DHT has a good interface • Put(key, value) and get(key)  value • Simple interface! • API supports a wide range of applications • DHT imposes no structure/meaning on keys • Key/value pairs are persistent and global • Can store keys in other DHT values • And thus build complex data structures

10. A DHT makes a good shared infrastructure • Many applications can share one DHT service • Much as applications share the Internet • Eases deployment of new applications • Pools resources from many participants • Efficient due to statistical multiplexing • Fault-tolerant due to geographic distribution

11. Many recent DHT-based projects • File sharing [CFS, OceanStore, PAST, Ivy, …] • Web cache [Squirrel, ..] • Backup store [Pastiche] • Censor-resistant stores [Eternity, FreeNet,..] • DB query and indexing [Hellerstein, …] • Event notification [Scribe] • Naming systems [ChordDNS, Twine, ..] • Communication primitives [I3, …] Common thread: data is location-independent

12. CFS: Cooperative file sharing • DHT is a robust block store • Client of DHT implements file system • Read-only: CFS, PAST • Read-write: OceanStore, Ivy File system block get (key) put (key, block) Distributed hash tables …. node node node

13. File representation:self-authenticating data File System key=995 431=SHA-1 144 = SHA-1 901= SHA-1 … 995: key=901 key=732 Signature key=431 key=795 “a.txt” ID=144 … (i-node block) … … (data) (root block) (directory blocks) • Key = SHA-1(content block) • File and file systems form Merkle hash trees

14. DHT distributes blocks by hashing IDs Block 732 Block 705 Node B 995: key=901 key=732 Signature 247: key=407 key=992 key=705 Signature Node A Internet Block 407 Node C Node D Block 901 Block 992 • DHT replicates blocks for fault tolerance • DHT caches popular blocks for load balance

15. Historical web archiver • Goal: make and archive a daily check point of the Web • Estimates: • Web is about 57 Tbyte, compressed HTML+img • New data per day: 580 Gbyte • 128 Tbyte per year with 5 replicas • Design: • 12,810 nodes: 100 Gbyte disk each and 61 Kbit/s per node

16. Implementation using DHT Crawler Client put(sha-1(URL), page) get (URL) • DHT usage: • Crawler distributes crawling and storage load by hash(URL) • Client retrieve Web pages by hash(URL) • DHT replicates data for fault tolerance Distributed hash tables …. node node node

17. Backup store • Goal: backup on other user’s machines • Observations • Many user machines are not backed up • Backup requires significant manual effort • Many machines have lots of spare disk space • Using DHT: • Merkle tree to validate integrity of data • Administrative and financial costs are less for all participants • Backups are robust (automatic off-site backups) • Blocks are stored once, if key = sha1(data)

18. DHT implementation challenges • Scalable lookup • Balance load (flash crowds) • Handling failures • Coping with systems in flux • Network-awareness for performance • Robustness with untrusted participants • Programming abstraction • Heterogeneity • Anonymity • Indexing Goal: simple, provably-good algorithms this talk

19. 1. The lookup problem N2 N1 N3 Put (Key=sha-1(data), Value=data…) Internet ? Client Publisher Get(key=sha-1(data)) N4 N6 N5 • Get() is a lookup followed by check • Put() is a lookup followed by a store

20. Centralized lookup (Napster) N2 N1 SetLoc(“title”, N4) N3 Client DB N4 Publisher@ Lookup(“title”) Key=“title” Value=file data… N8 N9 N7 N6 Simple, but O(N) state and a single point of failure

21. Flooded queries (Gnutella) N2 N1 Lookup(“title”) N3 Client N4 Publisher@ Key=“title” Value=MP3 data… N6 N8 N7 N9 Robust, but worst case O(N) messages per lookup

22. K5 K20 N105 Circular ID space N32 N90 K80 N60 Algorithms based on routing • Map keys to nodes in a load-balanced way • Hash keys and nodes into a string of digit • Assign key to “closest” node • Forward a lookup for a key to a closer node • Join: insert node in ring Examples: CAN, Chord, Kademlia, Pastry, Tapestry, Viceroy, ….

23. Chord’s routing table: fingers ½ ¼ 1/8 1/16 1/32 1/64 1/128 N80

24. Lookups take O(log(N)) hops N5 N10 N110 K19 N20 N99 N32 Lookup(K19) N80 N60 • Lookup: route to closest predecessor

25. (0,1) (0.5,0.5, 1, 1) • • (0,0.5, 0.5, 1) (0.5,0.25, 0.75, 0.5) • • • (0.75,0, 1, 0.5) (0,0, 0.5, 0.5) (0,0) (1,0) CAN: exploit d dimensions • Each node is assigned a zone • Nodes are identified by zone boundaries • Join: chose random point, split its zone

26. Routing in 2-dimensions (0,1) (0.5,0.5, 1, 1) • Routing is navigating a d-dimensional ID space • Route to closest neighbor in direction of destination • Routing table contains O(d) neighbors • Number of hops is O(dN1/d) • • (0,0.5, 0.5, 1) (0.5,0.25, 0.75, 0.5) • • • (0.75,0, 1, 0.5) (0,0, 0.5, 0.5) (0,0) (1,0)

27. 2. Balance load N5 K19 N10 N110 K19 N20 N99 N32 Lookup(K19) N80 N60 • Hash function balances keys over nodes • For popular keys, cache along the path

28. Why Caching Works Well N20 • Only O(log N) nodes have fingers pointing to N20 • This limits the single-block load on N20

29. K19 K19 K19 3. Handling failures: redundancy N5 N10 N110 N20 N99 N32 N40 N80 N60 • Each node knows IP addresses of next r nodes • Each key is replicated at next r nodes

30. Lookups find replicas N5 N10 N110 3. N20 1. 2. N99 K19 N40 4. N50 N80 N68 N60 Lookup(K19) • Tradeoff between latency and bandwidth [Kademlia]

31. 4. Systems in flux • Lookup takes log(N) hops If system is stable But, system is never stable! • What we desire are theorems of the type: • In the almost-ideal state, ….log(N)… • System maintains almost-ideal state as nodes join and fail

32. Half-life [Liben-Nowell 2002] N new nodes join N nodes • Doubling time: time for N joins • Halfing time: time for N/2 old nodes to fail • Half life: MIN(doubling-time, halfing-time) N/2 old nodes leave

33. Applying half life • For any node u in any P2P networks: If u wishes to stay connected with high probability, then, on average, u must be notified about (log N) new nodes per half life • And so on, …

34. 5. Optimize routing to reduce latency N20 • Nodes close on ring, but far away in Internet • Goal: put nodes in routing table that result in few hops and low latency N40 N41 N80

35. “close” metric impacts choice of nearby nodes N06 USA N105 • Chord’s numerical close and table restrict choice • Prefix-based allows for choice • Kademlia’s offers choice in nodes and places nodes in absolute order: close (a,b) = XOR(a, b) USA K104 Far east N103 N32 Europe N60 USA

36. Neighbor set N06 USA USA N105 • From k nodes, insert nearest node with appropriate prefix in routing table • Assumption: triangle inequality holds K104 N103 N32 Far east Europe N60 USA

37. Finding k near neighbors • Ping random nodes • Swap neighbor sets with neighbors • Combine with random pings to explore • Provably-good algorithm to find nearby neighbors based on sampling [Karger and Ruhl 02]

38. 6. Malicious participants • Attacker denies service • Flood DHT with data • Attacker returns incorrect data [detectable] • Self-authenticating data • Attacker denies data exists [liveness] • Bad node is responsible, but says no • Bad node supplies incorrect routing info • Bad nodes make a bad ring, and good node joins it • Basic approach: use redundancy

39. Sybil attack [Douceur 02] N5 • Attacker creates multiple identities • Attacker controls enough nodes to foil the redundancy N10 N110 N20 N99 N32 N40 N80 N60 • Need a way to control creation of node IDs

40. One solution: secure node IDs • Every node has a public key • Certificate authority signs public key of good nodes • Every node signs and verifies messages • Quotas per publisher

41. Another solution:exploit practical byzantine protocols N06 N105 N • A core set of servers is pre-configured with keys and perform admission control • The servers achieve consensus with a practical byzantine recovery protocol [Castro and Liskov ’99 and ’00] • The servers serialize updates [OceanStore] or assign secure node Ids [Configuration service] N N N103 N32 N N60

42. A more decentralized solution:weak secure node IDs • ID = SHA-1 (IP-address node) • Assumption: attacker controls limited IP addresses • Before using a node, challenge it to verify its ID

43. Using weak secure node IDS • Detect malicious nodes • Define verifiable system properties • Each node has a successor • Data is stored at its successor • Allow querier to observe lookup progress • Each hop should bring the query closer • Cross check routing tables with random queries • Recovery: assume limited number of bad nodes • Quota per node ID

44. Philosophical questions • How decentralized should systems be? • Gnutella versus content distribution network • Have a bit of both? (e.g., OceanStore) • Why does the distributed systems community have more problems with decentralized systems than the networking community? • “A distributed system is a system in which a computer you don’t know about renders your own computer unusable” • Internet (BGP, NetNews)

45. What are we doing at MIT? • Building a system based on Chord • Applications: CFS, Herodotus, Melody, Backup store,Ivy, … • Collaborate with other institutions • P2P workshop, PlanetLab • Big ITR • Building a large-scale testbed • RON, PlanetLab

46. Summary • Once we have DHTs, building large-scale, distributed applications is easy • Single, shared infrastructure for many applications • Robust in the face of failures and attacks • Scalable to large number of servers • Self configuring across administrative domains • Easy to program • Let’s build DHTs …. stay tuned …. http://project-iris.net

47. 7. Programming abstraction • Blocks versus files • Database queries (join, etc.) • Mutable data (writers) • Atomicity of DHT operations