1. Distributed Hash Tables
David Tam
Patrick Pang

2. 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

3. Distributed application Distributed hash table
What is DHT?
Distributed application
put(key, data)
get (key)
data
Distributed hash table
node ….
DHT provides the information look up service for P2P 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
Make it simple to write decentralized, distributed applications …..
(Figure adopted from Frans Kaashoek)

4. 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 P2P resources

5. 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 ?

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

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

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

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

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

11. How lookup works? Example: Chord 1 15 2 14 31 15 2 14 3
Now Node 2 can retrive information for key 0 from Node 1. 4 12 5 11 10 6 7 9 8

12. Alternatives to DHTs N4 N6 N9 N7 N8 N3 N2 N1 N10 N4 N6 N9 N7 N8 N3 N2
Target
Start N6 N9 N7 N8 N3 N2 N1
N10
Distributed file system
Centralized lookup
P2P flooding queries
Server
Client
Internet N4
Target
Start N6 N9 N7 N8 N3 N2 N1
N10 DB
(Figures adopted from Frans Kaashoek)

13. 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

14. 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

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

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

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

18. 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]

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

20. 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]

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

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

23. Comparison to Other Facilities
Facility
Abstraction
Easy Use/Prg
Scalability
Load-Balance
DHT
high
yes
Centralized Lookup
medium
low no
P2P flooding queries
Distributed FS
Facility
Fault-Tolerance
Self-Org
Admin
DHT
high
yes
low
Centralized Lookup no
medium
P2P flooding queries
depends
Distributed FS

24. 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

25. Summary Good middleware platform Exploits P2P networks
An exciting new research area

26. References Lamport, Leslie et. al. The Byzantine Generals Problem
Sit, Emil, Morris, Robert. Security Considerations for Peer-to-Peer Distributed Hash Tables
Kaashoek, Frans. Distributed Hash Tables – Building large-sacle, 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