Topology-Aware Overlay Networks By Huseyin Ozgur TAN

Outline Introduction CAN & eCAN Generating Proximity Information Exploiting Topology Conclusion

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

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

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 ?

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

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

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

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

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

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

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)

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