1 Detecting and Reducing Partition Nodes in Limited-routing-hop Overlay Networks Zhenhua Li and Guihai Chen State Key Laboratory for Novel Software Technology.

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Presentation transcript:

1 Detecting and Reducing Partition Nodes in Limited-routing-hop Overlay Networks Zhenhua Li and Guihai Chen State Key Laboratory for Novel Software Technology Nanjing University, Nanjing, P. R. China

2 Background Overlay networks - base infrastructures of many Internet applications Limited routing hops - routing one hop in the overlay network is much more expensive than that in the underlying network. - flooding or flooding-based routing mechanism - so has a limit called TTL

3 Motivation Overlay partition - seriously degrade the system performance Existence of topologically-critical nodes - some nodes’ failure will cause overlay partition with much higher possibility than others

4 Related work (1) Proactive avoid and Event driven - using a centralized server to direct nodes’ join and leave - but the server becomes a single point of failure Proactive avoid and Periodical detect - CAM: actively detect cut nodes and then neutralize them into normal nodes - but cut nodes are not applicable to limited-routing-hop overlay networks

5 Related work (2) Reactive recover and Event driven - ring partition detect and repair on Pastry and SkipNet - but they can only be used on ring topology Reactive recover and Periodical detect - cross-check method: ask other nodes to do random queries and compare their results with its own - but it has much randomness and uncertainty of detection

6 The concept of partition node - topologically-critical nodes of limited-routing-hop overlay networks Partition node detection and reduction - a distributed proactive method to detect partition nodes - reduce partition nodes by changing them to normal nodes - greatly enhance the connectivity and fault tolerance of overlay networks Our proposed ideas

7 Outline Partiton node concept Partition node detection Partition node reduction Performance evaluation

8 Partition node concept (1) Cut node vs. partition node. - (a) (b) C is a cut node because when C fails, the overlay network is partitioned; - (c) (d) C is a partition node because when C fails, the overlay network is not partitioned, but C’s neighbors 1, 3, 5, 7 can no longer find each other.

9 Partition node concept (2) Definition 1 (Locatability) In a limited-routing-hop overlay network, node A could locate node B only if A can find B by sending routing messages. It is denoted by A → B. Definition 2 (Reachability) In a limited-routing-hop overlay network, node A could reach node C if A can locate C, or A can locate some node B and B can locate C. It is denoted by A→→C.

10 Partition node concept (3) Example: Node 1 can only locate nodes 2, 3, 4, and can reach node 5, 6, 7, but cannot reach node 8.

11 Partition node concept (4) Definition 3 (Partition Node) Node C is a partition node if C’s neighbor set would be partitioned into two or more unreachable subsets S1, S2,..., Sn (n≥2) when C fails. Example:

12 Partition node detection (1) 4 steps: Initialize detection (0)(1) Probe reachability (2a)(2b) Partition subsets (3) Make decision (4)

13 Partition node detection (2)

14 Partition node reduction (1) Add edges to reduce partition nodes - choose an appropriate delegate node Ni from each subset Si, - and then connects all the delegate nodes in some way. - In order to improve the system’s fault tolerance, we try to make every node’s degree above a constant lower bound as much as possible.

15 Partition node reduction (2) Linear chain connection vs. Chordal ring connection - more edges, but much more resilience

16 Partition node reduction (3) Remove edges to limit node degree - the new edges added to reduce a partition node cannot be removed; - remove the edge whose corresponding node has the highest load factor. Total cost of partition node detection and reduction - n: tatal number of nodes, t: TTL, c: average node degree - total cost is

17 Performance evaluation (1) Partition nodes’ significance to overlay topology.

18 Performance evaluation (2) Effectiveness of our method

19 Performance evaluation (3) Fault tolerance improvement

20 The End Thanks!