vdr: proactive element conclusions vdr reaches 3.5% more nodes than vdr-r and 9% more nodes than our...

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VDR: Proactive element Conclusions VDR reaches 3.5% more nodes than VDR-R and 9% more nodes than our modified random walk routing strategy (RWR) VDR shows a 3-4X reach retention rate going from 0% to 50% network churn compared to VDR-R and RWR, showing itself to be much more robust to network churn VDR increases reach with fewer number of virtual interfaces because of its biasing technique. Gains disappear if the number of neighbors is smaller than the number of interfaces • Increasing the number of neighbors generally increases reach and end-to-end path stretch VDR states are not well distributed and states and load is not spread evenly VDR paths exhibit high path stretch compared to shortest path but good path stretch compared to pure random walk Virtual Direction Routing (VDR) for Overlay Networks Bow-Nan Cheng (RPI), Murat Yuksel (UNR), Shivkumar Kalyanaraman (RPI) Motivation •The explosion of peer-to-peer systems in recent years has prompted research into finding scalable and robust seeding and searching methods to support these overlay networks. Initial work relied on network flooding to find information and while robust, lacked scalability. In effort to scale large networks, many have looked at structured approaches to the problem by imposing some sort of structure to the network topology and routing based on that structure. To support search queries, a robust overlay network with routing policies must be in place as search fails to address the actual data traversal path. In much the same way, routing in overlay networks have evolved from pure flooding techniques to structured techniques. In our work, we attempt to drop the need for imposing a specific structure on the overlay network and introduce a technique to scalably route packets through an unstructured overlay network. VDR Details VDR Introduction VDR – 2 Basic Primitives 1.Local directionality is sufficient to maintain forwarding of a packet on a straight line 2.Two sets of orthogonal lines in a plane intersect with high probability even in sparse, bounded networks Simulation Results 180 o S T VDR Primitive - Local sense of direction leads to ability to forward packets in opposite directions Rendezvous Points A B Flood-based Hierarchy/Structured Unstructured/Flat Scalable Trends – From Flood-based to Unstructured Scalable Paths from Rendezvous Nodes-to-Destination Nodes are formed by periodically sending announcement packets out orthogonal directions Relevant Publications • B. Cheng, M. Yuksel, S. Kalyanaraman, “Virtual Direction Routing for Overlay Networks," Proceedings of IEEE International Conference on Peer-to-Peer Computing (P2P), Seattle WA, September 2009. This material is based upon work supported by the National Science Foundation under Grants 0627039, 0721452, 0721612 and 0230787. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. Ex: Seed Source: Node 1 State Seeding – State info forwarded in orthogonal directions, biasing packets toward IDs that are closer to SOURCE ID. Packets are forwarded in virtual straight lines. Ex: Route Request: Node 12 RREQ Source: Node 1 Route Request RREQ packets are forwarded in orthogonal directions, biasing packets towards REQUESTED ID 10 26 48 15 30 68 1 2 0 7 6 5 4 3 67 13 28 1 2 0 7 6 5 4 3 55 22 1 2 0 7 6 5 4 3 14 1 10 5 5 10 1 |10 1| = 9 |26 – 1| = 25 |5 1| = 4 |13 – 1| = 12 |14 1| = 13 |22 – 1| = 21 10 26 48 15 30 68 1 2 0 7 6 5 4 3 67 13 28 1 2 0 7 6 5 4 3 6 1 2 0 7 6 5 4 3 38 1 10 5 13 10 1 |10 12| = 2 |26 – 12| = 15 |5 12| = 7 |13 – 12| = 1 |6 12| = 6 |38 – 12| = 26 VDR: Reactive element Paths from Source Nodes-to-Rendezvous Nodes are formed by sending route request (RREQ) packets and waiting for route reply (RREP) packets. These RREQ packets are sent on demand 1 30 15 10 26 68 1 2 0 7 6 5 4 3 30 5 26 10 68 15 30 % 8 = 6 10 % 8 = 2 26 % 8 = 2 68 % 8 = 4 15 % 8 = 7 Neighbors are either physical neighbors connected by interfaces or neighbors under a certain RTT latency away (logical neighbors) Neighbor to Virtual Interface Mapping Each neighbor ID is hashed to 160 bit IDs using SHA-1 (to standardize small or large IDs) The virtual interface assigned to the neighbor is a function of its hashed ID (Hashed ID % number of virtual interfaces) 5% drop 15% drop 12% drop Question: Can 1 hop neighbors in overlay networks be consistently “mapped” to a local virtual direction such that be forwarding in virtual orthogonal lines, a high chance of intersection (and search success) results? Structured vs. Unstructured Overlay Networks Unstructured P2P systems make little or no requirement on how overlay topologies are established and are easy to build and robust to churn Typical Search Technique (Unstructured Networks) Flooding / Normalized Flooding •High Reach, Low path stretch, Not scalable Random Walk •Need high TTL for high reach, Long paths, Scalable, but hard to find rare objects Virtual Direction Routing •Globally consistent sense of direction (west is always west) Scalable interface to neighbor mapping •Routing can be done similarly to ORRP Focus (for now) •Small world approximations Random Walk Virtual Direction Routing Flooding Normalized Flooding Evaluation Metrics / Scenarios Reachability – Percentage of nodes reachable by each node in network State Complexity – The total state info (+ spread) maintained in the network Average End-to-End Path Stretch Average VDR Path vs. Shortest Path Average Load Network-wide Effect of Seed/Query TTL on VDR, VDR-R, and RWR Effect of # of Virtual Interfaces on VDR, VDR-R, and RWR Effect of Average # of Neighbors on VDR, VDR-R, and RWR Effect of % Network Churn on VDR, VDR-R, and RWR Metrics Packetized Simulations with PeerSim Scenarios Parameter Default Values Nodes / # of Virt Intf 50,000 / 8 Simulation Cycles 150 Churn Percentage 0% - 50% every 5 cycles Seed/RREQ TTL 10 – 100 hops Seed Entry Expiry 10 Cycles (under churn) Number of Queries 1000 Randomly Gen. Default Simulation Parameters VDR-R: VDR with random neighbor forwarding (no biasing) RWR: Data is seeded in 4 random walks and 4 walkers are sent for search Comparison Protocols Flooding Random Walk VDR – Random NB Send (VDR- R) Virtual Direction Routing Normalized Flooding Random Walk Routing (RWR) RREQ: Node 12 Rendezvous Node Virtual View Seed Path RREQ Path 68 30 48 RREP Path 1 26 10 13 5 38 46 2 6 12 VDR Route Request VDR: Neighbor to Virtual Interface/Direction Mapping Neighbor to Virtual Interface Mapping Overlay Routing Proactive Element Reactive Element Two Components of VDR Virtual Direction Routing Virtual view of VDR Route Request process: Node 1 sends out a RREQ looking for Node 12. Once the RREQ intersects a rendezvous ndoe, a RREP is sent back. The virtual path of the data goes from the source node to the rendezvous to the

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Page 1: VDR: Proactive element Conclusions VDR reaches 3.5% more nodes than VDR-R and 9% more nodes than our modified random walk routing strategy (RWR) VDR shows

VDR: Proactive element

Conclusions• VDR reaches 3.5% more nodes than VDR-R and 9% more nodes than our modified random walk routing strategy (RWR)• VDR shows a 3-4X reach retention rate going from 0% to 50% network churn compared to VDR-R and RWR, showing itself to be much more robust to network churn• VDR increases reach with fewer number of virtual interfaces because of its biasing technique. Gains disappear if the number of neighbors is smaller than the number of interfaces• Increasing the number of neighbors generally increases reach and end-to-end path stretch• VDR states are not well distributed and states and load is not spread evenly• VDR paths exhibit high path stretch compared to shortest path but good path stretch compared to pure random walk

Virtual Direction Routing (VDR) for Overlay Networks

Bow-Nan Cheng (RPI), Murat Yuksel (UNR), Shivkumar Kalyanaraman (RPI)

Motivation•The explosion of peer-to-peer systems in recent years has prompted research into finding scalable and robust seeding and searching methods to support these overlay networks. Initial work relied on network flooding to find information and while robust, lacked scalability. In effort to scale large networks, many have looked at structured approaches to the problem by imposing some sort of structure to the network topology and routing based on that structure. To support search queries, a robust overlay network with routing policies must be in place as search fails to address the actual data traversal path. In much the same way, routing in overlay networks have evolved from pure flooding techniques to structured techniques. In our work, we attempt to drop the need for imposing a specific structure on the overlay network and introduce a technique to scalably route packets through an unstructured overlay network.

VDR Details

VDR IntroductionVDR – 2 Basic Primitives

1. Local directionality is sufficient to maintain forwarding of a packet on a straight line

2. Two sets of orthogonal lines in a plane intersect with high probability even in sparse, bounded networks

Simulation Results

180o

ST

VDR Primitive- Local sense of directionleads to ability to forwardpackets in opposite directions

Rendezvous Points

A

B

Flood-based Hierarchy/Structured Unstructured/Flat Scalable

Trends – From Flood-based to Unstructured Scalable

Paths from Rendezvous Nodes-to-Destination Nodes are formed by periodically sending announcement packets out orthogonal directions

Relevant Publications• B. Cheng, M. Yuksel, S. Kalyanaraman, “Virtual Direction Routing for Overlay Networks," Proceedings of IEEE International Conference on Peer-to-Peer Computing (P2P), Seattle WA, September 2009.

This material is based upon work supported by the National Science Foundation under Grants 0627039, 0721452, 0721612 and 0230787. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Ex: Seed Source: Node 1

State Seeding – State info forwarded in orthogonal directions, biasing packets toward IDs that are closer to SOURCE ID. Packets are forwarded in virtual straight lines.

Ex: Route Request: Node 12RREQ Source: Node 1

Route Request – RREQ packets are forwarded in orthogonal directions, biasing packets towards REQUESTED ID

1026

48

15

30

68

120

765

4

367

13

28

120

765

4

3

55

22

120

765

4

3

14

1 10

5

5

101|10 – 1| = 9|26 – 1| = 25

|5 – 1| = 4|13 – 1| = 12

|14 – 1| = 13|22 – 1| = 21

1026

48

15

30

68

120

765

4

367

13

28

120

765

4

3

6

120

765

4

3

38

1 10

5

13

101|10 – 12| = 2|26 – 12| = 15

|5 – 12| = 7|13 – 12| = 1

|6 – 12| = 6|38 – 12| = 26

VDR: Reactive elementPaths from Source Nodes-to-Rendezvous Nodes are formed by sending route request (RREQ) packets and waiting for route reply (RREP) packets. These RREQ packets are sent on demand

1

3015

10

2668

120

765

4

3

30

5

26

10

68 15

30 % 8 = 6

10 % 8 = 2

26 % 8 = 2 68 % 8 = 4

15 % 8 = 7

Neighbors are either physical neighbors connected by interfaces or neighbors under a certain RTT latency away (logical neighbors)

Neighbor to Virtual Interface Mapping

Each neighbor ID is hashed to 160 bit IDs using SHA-1 (to standardize small or large IDs)

The virtual interface assigned to the neighbor is a function of its hashed ID (Hashed ID % number of virtual interfaces)

5% drop

15% drop

12% drop

Question: Can 1 hop neighbors in overlay networks be consistently “mapped” to a local virtual direction such that be forwarding in virtual orthogonal lines, a high chance of intersection (and search success) results?

•Structured vs. Unstructured Overlay Networks•Unstructured P2P systems make little or no requirement on how overlay topologies are established and are easy to build and robust to churn

•Typical Search Technique (Unstructured Networks)•Flooding / Normalized Flooding

•High Reach, Low path stretch, Not scalable

•Random Walk•Need high TTL for high reach, Long paths, Scalable, but hard to find rare objects

•Virtual Direction Routing•Globally consistent sense of direction (west is always west) Scalable interface to neighbor mapping•Routing can be done similarly to ORRP

•Focus (for now)•Small world approximations

Random Walk

Virtual DirectionRouting

Flooding

Normalized Flooding

Evaluation Metrics / Scenarios

• Reachability – Percentage of nodes reachable by each node in network

• State Complexity – The total state info (+ spread) maintained in the network

• Average End-to-End Path Stretch – Average VDR Path vs. Shortest Path

• Average Load Network-wide

• Effect of Seed/Query TTL on VDR, VDR-R, and RWR• Effect of # of Virtual Interfaces on VDR, VDR-R, and RWR• Effect of Average # of Neighbors on VDR, VDR-R, and RWR• Effect of % Network Churn on VDR, VDR-R, and RWR

Metrics

Packetized Simulations with PeerSim Scenarios

Parameter Default Values

Nodes / # of Virt Intf 50,000 / 8

Simulation Cycles 150

Churn Percentage 0% - 50% every 5 cycles

Seed/RREQ TTL 10 – 100 hops

Seed Entry Expiry 10 Cycles (under churn)

Number of Queries 1000 Randomly Gen.

Default Simulation Parameters

• VDR-R: VDR with random neighbor forwarding (no biasing)• RWR: Data is seeded in 4 random walks and 4 walkers are

sent for search

Comparison Protocols

Flooding

Random Walk

VDR – Random NB Send (VDR-R)

Virtual Direction Routing

Normalized Flooding

Random Walk Routing (RWR)

RREQ: Node 12

Rendezvous Node

Virtual View

Seed Path

RREQ Path

68

30

48

RREP Path

126

1013

5

38

46

2

6

12VDR Route Request

VDR: Neighbor to Virtual Interface/Direction Mapping

Neighbor to Virtual Interface Mapping

Overlay Routing •Proactive Element•Reactive Element

Two Components of VDR

Virtual DirectionRouting

Virtual view of VDR Route Request process: Node 1 sends out a RREQ looking for Node 12. Once the RREQ intersects a rendezvous ndoe, a RREP is sent back. The virtual path of the data goes from the source node to the rendezvous to the destionation