Distributed Hash Tables David Tam Patrick Pang Presentation

Distributed Hash Tables David Tam Patrick Pang

Presentation Outline • What is DHT (Distributed Hash Table)? • Why DHTs? • Applications • How lookup works? • Alternatives to DHTs • Performance – Routing • Performance – Load Balancing • Security – Routing Attack • Security – Inconsistent Behaviour • Comparison to Other Facilities • Current Research Projects • Conclusion

What is DHT? Distributed application put(key, data) node get (key) Distributed hash table node …. data node DHT provides the information look up service for P 2 P applications. • Nodes uniformly distributed across key space • Nodes form an overlay network • Nodes maintain list of neighbours in routing table • Decoupled from physical network topology (Figure adopted from Frans Kaashoek)

Why DHTs? Why Middleware? • Simplifies the development for large-scale distributed Apps • Better security and robustness • Simple API Why Do We Need DHTs? • Simplifies the development for large-scale distributed Apps • Better security and robustness • Simple API • Exploits P 2 P resources

Applications • Anything that requires a hash table • Databases, FSes, storage, archival • Web serving, caching • Content distribution • Query & indexing • Naming systems • Communication primitives • Chat services • Application-layer multi-casting • Event notification services • Publish/subscribe systems ?

How lookup works? Example: Chord [Stoica et. al. ] 0 15 Finger Table for Node 2 start interval 3 [3, 4) succ. 5 4 5 6 10 [4, 6) [6, 10) [10, 2) 7 10 1 2 14 3 13 4 12 5 11 10 7 9 8 6

How lookup works? Example: Chord 0 15 Finger Table for Node 10 start interval 11 [11, 12) succ. 12 12 12 14 2 [12, 14) [14, 2) [2, 10) 14 2 14 3 13 4 12 5 11 10 7 9 8 6

How lookup works? Example: Chord 0 15 Finger Table for Node 10 start interval 11 [11, 12) succ. 12 12 12 14 2 [12, 14) [14, 2) [2, 10) 14 2 14 3 13 4 12 5 11 10 7 9 8 6

How lookup works? Example: Chord 0 15 Finger Table for Node 14 start interval 15 [15, 0) succ. 15 0 1 2 6 [0, 2) [2, 6) [6, 13) 2 7 1 2 14 3 13 4 12 5 11 10 7 9 8 6

How lookup works? Example: Chord 0 15 Finger Table for Node 14 start interval 15 [15, 0) succ. 15 0 1 2 6 [0, 2) [2, 6) [6, 13) 2 7 1 2 14 3 13 4 12 5 11 10 7 9 8 6

How lookup works? Example: Chord 0 15 1 2 14 Now Node 2 can retrive information for key 0 from Node 1. 3 4 12 5 11 10 7 9 8 6

Alternatives to DHTs • Distributed file system • Centralized lookup • P 2 P flooding queries N 1 N 4 Internet N 9 N 1 Client N 4 Target Client Server N 3 Target Server Client N 2 N 9 N 6 N 10 N 7 N 8 N 2 DB N 6 Start N 3 N 7 N 8 Start N 10 (Figures adopted from Frans Kaashoek)

Performance -- Lookup Purpose -- to locate a target node • Each step, try to get closer to locating target node • Ask a closer neighbour • Performance & scalability tied directly to lookup algorithm 2 Aspects to Performance • Path latency • Lookup path length (# hops) 2 Aspects to Scalability • size of routing table – O(log N) • lookup path length – O(log N) 3 Techniques • proximity lookup • proximity neighbour selection • geographic layout

Performance -- Load Balancing Issues • Hot-spots • Content • Lookup • Heterogeneous nodes & paths • System flux Solution • Replication is the key • Also good for fault-tolerance • Cache lookup answers backwards along path

Security – Incorrect Lookup (1) • When asked for the “next hop”, give a wrong answer Finger Table for Node 2 start interval succ. 3 [3, 4) 5 4 [4, 6) 5 6 [6, 10) 7 10 [10, 2) 10 Node 2 to Node 10: Please tell me how to reach key 0 …. 14 15 0 1 2 13 3 4 12 11 5 10 9 7 8 6

Security – Incorrect Lookup (2) • When asked for the “next hop”, give a wrong answer Finger Table for Node 10 start interval succ. 11 [11, 12) 12 12 [12, 14) 12 14 [14, 2) 14 2 [2, 10) 2 Node 2 to Node 10: Please tell me how to reach key 0 …. Node 10 answers: ask Node 14 14 15 0 1 2 13 3 4 12 11 5 10 9 7 8 6

Security – Incorrect Lookup (3) • When asked for the “next hop”, give a wrong answer Finger Table for Node 14 start interval succ. 15 [15, 0) 15 0 [0, 2) 1 2 [2, 6) 2 6 [6, 13) 7 Node 2 to Node 14: Please tell me how to reach key 0 …. Node 14 answers: ask Node 10 14 15 0 1 2 13 3 4 12 11 5 10 9 7 8 6

Security – Incorrect Lookup (4) Solution [Sit and Morris]: • “Define verifiable system invariant” • “Allow the querier to observe lookup progress” Our idea how this can be implemented: • Concretely, using an integral monotonically decreasing quantity to implement the idea of “progress”. • The concept of “monotonically decreasing quantity” has been used in program construction guaranteeing total correctness. [Parnas]

Security – Inconsistent Behaviour • Inconsistent Behaviour, i. e. , lie intelligibly • Sybil attack [Kaashoek] Solution 1: public key solution

Security – Inconsistent Behaviour • Inconsistent Behaviour, i. e. , lie intelligibly • Sybil attack [Kaashoek] Solution 1: public key solution Solution 2: Byzantine Protocol Byzantine Generals Problem: How to find out the traitors among the Generals? [Lamport]

Security – Inconsistent Behaviour • Inconsistent Behaviour, i. e. , lie intelligibly • Sybil attack [Kaashoek] Solution 1: public key solution Solution 2: Byzantine Protocol Commander Byzantine Generals Problem: attack How to find out the traitors among the Generals? [Lamport] Lieutenant 1 “he said ‘retreat’” Lieutenant 2

Security – Inconsistent Behaviour • Inconsistent Behaviour, i. e. , lie intelligibly • Sybil attack [Kaashoek] Solution 1: public key solution Solution 2: Byzantine Protocol Commander Byzantine Generals Problem: attack retreat How to find out the traitors among the Generals? [Lamport] Lieutenant 1 “he said ‘retreat’” Lieutenant 2

Comparison to Other Facilities Facility Abstraction Easy Use/Prg Scalability Load-Balance DHT high yes Centralized Lookup medium low no P 2 P flooding queries medium high low no Distributed FS low medium no Facility Fault-Tolerance Self-Org Admin DHT high yes low Centralized Lookup low no medium P 2 P flooding queries depends yes low Distributed FS no high medium

Research Projects Iris – security & fault-tolerance – US Gov’t Chord – circular key space Pastry – circular key space Tapestry – hypercube space CAN – n-dimensional key space Kelips – n-dimensional key space DDS -- middleware platform for internet service construction -- cluster-based -- incremental scalability

Summary • Good middleware platform • Exploits P 2 P networks • An exciting new research area

References • Lamport, Leslie et. al. The Byzantine Generals Problem • Sit, Emil, Morris, Robert. Security Considerations for Peerto-Peer Distributed Hash Tables • Kaashoek, Frans. Distributed Hash Tables – Building largesacle, robust distributed applications • Stoica, Ion et. al. Chord: A scalable peer-to-peer lookup service for Internet applications • Parnas, D. L. Connecting Theory to Practice: Software Engineering Programme