1. Virtual Direction Routing
Murat Yuksel
Bow-Nan Cheng
Shivkumar Kalyanaraman

2. Scaling Networks: Trends in Layer 3
Flood-based
Hierarchy/Structured
Unstructured/Flat
Scalable
Mobile Ad hoc /
Fixed Wireless
Networks
DSR, AODV,
TORA, DSDV
Partial Flood:
OLSR, HSLS
LGF, VRR, GPSR+GLS
Hierarchical Routing,
WSR (Mobicom 07)
ORRP (ICNP 06)
Peer to Peer /
Overlay Networks
Kazaa, DHT Approaches:
CHORD, CAN
BubbleStorm (Sigcomm 07)
LMS (PODC 05)
Gnutella
Ethernet
Routers (between AS)
SEIZE
Wired Networks

3. Virtual Direction Routing Introduction
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
Flooding
Normalized Flooding
Virtual Direction
Routing
Random Walk

4. VDR Conceptualization
Virtual Direction
Routing (VDR) Protocol
Primitives
1: Local sense of direction leads to ability to forward packets in opposite directions A
180o
98% S T
Up to 69%
2: Forwarding along
Orthogonal lines has a high chance of intersection in area B
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?

5. Wireless Nets: Key Concepts to Abstract
Primitives:
Local directionality is enough to maintain forwarding along a straight line
Two sets of orthogonal lines intersect with a high probability in a bounded region
Overlay Networks:
Can we take these concepts to scale unstructured, flat, overlay networks?
Two Focuses:
Neighbor to virtual direction/interface mapping
Routing scheme based on virtual direction mapping

6. Neighbor to Virtual Interface
/ Direction Mapping

7. VDR: Neighbor to Virtual Interface Map
Example: Neighbor IDs used
Instead Of SHA-1 Hashes
30 % 8 = 6 7 1 2 3 4 5 6
8 Virtual Interfaces 26 30
15 % 8 = 7
10 % 8 = 2 15 10 10 1 15 26 68 30
26 % 8 = 2 68
68 % 8 = 4
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)

8. Virtual Direction Routing

9. VDR Proactive and Reactive Elements
VDR conceptual representation of Proactive and Reactive Elements
Node B Fwd Table
Dest
Next
Hops A 1
Node C Fwd Table
Dest
Next
Hops
Dir A F 3
120o D 1
230o
North
North
North
120o D C F B A
230o
North
Node F Fwd Table
Dest
Next
Hops A B 2
A to D
North
Virtual View
VDR State Seeding (Proactive) – Generates Rendezvous-to-Destination Routes
VDR Route Request (RREQ) Packets (Reactive) – Generates Source-to-Rendezvous Rts
VDR Route Reply (RREP) Packets (Reactive)
VDR path after route generation

10. VDR: State Seeding and Route Request
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. 1 2 3 4 5 6 7 26 30 15 68 48 10 10
|10 – 1| = 9
|26 – 1| = 25 10 1 2 3 4 5 6 7 67 13 28 5 1 2 3 4 6 7 55 10 22 14
|14 – 1| = 13
|22 – 1| = 21
|5 – 1| = 4
|13 – 1| = 12 5 14
Ex: Seed Source: Node 1 68
VDR State Seeding – Virtual View
Periodically, every node sends out Seed messages in virtual orthogonal directions
Seed Path 13 10 14 30 1 5 26 22
Seed Source: Node 1 48

11. VDR: State Seeding and Route Request68 1 2 3 4 5 6 7 10 26 30 15 48 10
Route Request – RREQ packets are forwarded in orthogonal directions, biasing packets towards REQUESTED ID 10 1 2 3 4 5 6 7 67 13 28 13 1 2 3 4 5 6 7 38 10
|10 – 12| = 2
|26 – 12| = 15 6
Ex: Route Request: Node 12
RREQ Source: Node 1
|5 – 12| = 7
|13 – 12| = 1
|6 – 12| = 6
|38 – 12| = 26 13
When a node wants to find a path to a destination, it sends out a route request (RREQ) packet in orthogonal directions with a bias 68
VDR Route Request – Virtual View
Seed Path 5 10 6 30 1 13 26 38
RREQ Source: Node 1
Route Request: Node 12 48

12. VDR: Comparing Seed and RREQ Paths
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. 1 2 3 4 5 6 7 26 30 15 68 48 10 10
|10 – 1| = 9
|26 – 1| = 25 10 1 2 3 4 5 6 7 67 13 28 5 1 2 3 4 6 7 55 10 22 14
|14 – 1| = 13
|22 – 1| = 21
|5 – 1| = 4
|13 – 1| = 12 5 14
Ex: Seed Source: Node 1 68 1 2 3 4 5 6 7 10 26 30 15 48 10
Route Request – RREQ packets are forwarded in orthogonal directions, biasing packets towards REQUESTED ID 10 1 2 3 4 5 6 7 67 13 28 13 1 2 3 4 5 6 7 38 10
|10 – 12| = 2
|26 – 12| = 15 6
Ex: Route Request: Node 12
RREQ Source: Node 1
|5 – 12| = 7
|13 – 12| = 1
|6 – 12| = 6
|38 – 12| = 26 13

13. VDR: Virtual View of Seed and RREQ46 68
RREQ Path 5
Rendezvous Node 10 6 30 1 13 26 38
RREP Path 2
RREQ: Node 12 48
Seed Path
VDR Route Request 12
Virtual View
Proactive Element
Every node sends out Seed packets in orthogonal directions at periodic intervals
Reactive Element
Route Request (RREQ) packets sent to search for rendezvous node.
When the RREQ intersects a node that has a path to the destination, a Route Reply (RREP) packet is sent back to the source

14. VDR: Simulation Parameters
Default Values
Nodes / # of Virt Interfaces
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 Generated
Flooding
Normalized Flooding
Virtual Direction
Routing
VDR – Random NB Send (VDR-R)
Simulation of VDR vs. RWR, VDR-R
VDR-R: VDR with random neighbor forwarding (no biasing)
RWR: Data is seeded in 4 random walks and 4 walkers are sent for search
PeerSim – 50,000 Nodes, Static + Dynamic Network
Reach Probability – High (98% w/ TTL of 100)
Average Path Stretch – High (16)
State and Load Spread – Not evenly distributed
Random Walk Routing (RWR)
Random Walk

15. VDR: Effect of Seed/Query TTL
Reach Probability to Seed/Query TTL
VDR reaches 98% of a network in a network of 50,000 nodes with a seed/query TTL of 100.
VDR converges to 100% reach more quickly than VDR-R or RWR
Avg. End-to-End Path Streetch to Seed/Query TTL
Average path stretch is less than VDR-R or RWR. This is due to the biasing effect of the Seed and RREQ packets

16. VDR: Effect of # of Virtual Interfaces
Reach Probability to Virtual # of Interfaces
VDR yields higher reach than VDR-R with fewer virtual interfaces. With 20 average neighbors, multiple neighbors can be assigned to an interface and so the biasing affect of VDR has more effect
Path stretch to # of Virtual Interfaces
VDR has lower average path stretch than VDR-R. VDR ensures that Seed and RREQ packets are biased toward it each other. Resulting in lower path stretch

17. VDR: Effect of Average # of Neighbors
Reach Probability to Avg # of Neighbors
As the number of neighbors increase, the more neighbors are mapped to a specific interface. The biasing effect of VDR has a greater effect on Seed and RREQ intersection
Path Stretch to Avg # of Neighbors
VDR’s biasing effect on Seed and RREQ packets lead to lower path stretch with greater number of neighbors because each interface has a higher number of neighbors mapped to it. The effect of biasing is more pronounced

18. VDR: Robustness Results
State Distribution Network-wide
Average States maintained relatively equal for VDR, VDR-R and RWR at
VDR States are not very evenly distributed, with some nodes having more state than others. This is due to the sending bias
Robustness to Network Churn
VDR drops only 5% compared to VDR-R and RWR which drop 12-15% reach when going from 0% to 50% network churn
Even with a TTL of 50, VDR reaches a good amount of the network
5% drop
15% drop
12% drop

19. VDR: Key Contributions
A flat, highly scalable, and resilient to churn routing algorithm for overlay networks
VDR provides high reach (98% even only for a TTL of 100 in a 50,000 node network)
VDR reaches 3.5% more nodes than VDR-R and 9% more nodes than our modified random walk routing strategy (RWR)
VDR-R produces the same reach and path stretch results with increasing number of virtual interfaces. VDR increases reach with fewer number of virtual interfaces because of biasing technique. Gains disappear if the number of neighbors is smaller than the number of interfaces
VDR states are not well distributed and load is not distributed evenly
VDR shows a 3-4X reach retention rate going from 0% to 50% network churn compared to VDR-R and RWR, showing istelf to be more robust to network churn
VDR paths exhibit high path stretch compared to shortest path but good path stretch compared to pure random walk

20. Thank You!
Questions and Comments?