Topology-Aware Overlay Networks

1. Topology-Aware Overlay Networks By Huseyin Ozgur TAN

2. Outline • Introduction • CAN & eCAN • Generating Proximity Information • Exploiting Topology • Conclusion

3. Introduction • Recent structured P2P systems (CAN, Chord, Pastry) • Administration free, fault tolerant DHT approach • Disadv: they do not take the advantage of the conditions of the underlying physical network • Utilizing Topology Information has 2 aspects • Generating Topology Information • Exploiting Topology

4. CAN • Content Addressable Network • d-dimensional Cartesian space • Partitioned into zones • Objects are points • Node owns the object point: responsible for it • Routing through a straight line • Random Point Selection in node joins

5. eCAN • Hierarchical CAN • CAN zones = order-1 • k order-i = order-(i+1) • Routing table includes high order neighbors • O(logN) routing • Flexible in selecting high order neighbors • Closest ?

6. Generating Proximity • IDMaps • Tracers • Tracers measure the latency among them & advertise it to clients • Distance between A and B = sum of • distance bw A and its closest tracer A’ • distance bw B and its closest tracer B’ • distance bw tracer A’ and B’ • Accuracy improves as the # of tracers increase

7. Generating Proximity • Expanding Ring Search • Contact the nodes that it knows • Contact the nodes within a radius by flooding • Measure RTTs and select the closest • Disadv: Large # of RTT measurements • Heuristics: Hill Climbing • Local Minimum

8. Generating Proximity • Landmark Clustering • Intuition: nodes close to each other have similar distances to a few selected landmark nodes • Measure RTTs with landmark nodes • Sort them in increasing order • The nodes with similar order are close • Disadv • Cannot differentiate nodes with the same landmark orders • False Clustering

9. Generating Proximity • Hybrid Approach • Landmark Clustering + RTT measurements • Landmark Clustering is pre-selection process to locate relatively close candidates • Measure RTTs to select the closest node • Stretch = the ratio of distance between A and its closest neighbor found by the algorithms to the distance between its ideal nearest neigbor

10. Generating Proximity • Conlusions • ERS is not effective -> heuristics are also • Landmark clustering can not find the closest neigbor • Finding the nearest neigbor in dense networks is harder than in sparse networks • However, hybrid approach improves quickly

11. Exploiting Topology • 3 known techniques • Proximity Routing • Geographic Layout • Proximity Neighbor Selection • Proximity Routing • overlay construction is unaware of topology • The message is forwarded to the topologically closest node among next hop candidates in routing table • Disadv: The candidates are limited with the routing table size

12. Exploiting Topology • Geographic Layout • The overlay structure is constrained by underlying network topology (topology-aware CAN) • Attempts to map the overlays local id space onto the physical network st. neighboring nodes are close in physical network • Disadvs: • Destroys Uniformity (5% of nodes occupy 85-98% of Cartesian space) • Does not work well in 1D approaches (Chord, Pastry etc)

13. Exploiting Topology • Proximity Neighbor Selection • Constructs a topology-aware overlay • Routing table entries refer to the topologically closest node among all nodes that satisfies the constraint of the logical overlay (Pastry : nodeID prefix) • Success depend on degree of freedom of logical structure • Not applicable to CAN or Chord