communication in dynamic networkssing.stanford.edu/slides/ian-dynamic.pdf · communication in...

Communication in Dynamic Networks

Ian Downes October 12 2010

Joint work with Omprakash Gnawali, Brano Kusy and Leonidas Guibas

Wednesday, October 13, 2010

Broad goals

• Understand dynamic networks

• Bounds on communication performance

• Distributed algorithms/protocols that perform well

2


Static

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• Routes between nodes are (nearly) static

• Build up next hop information for all destinations, e.g. use Dijkstra’s, repair as necessary

• Little concept of time

• Solved (OSPF,...)


Static with updates

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• Routes between nodes are (nearly) static

• Build up next hop information for all destinations, e.g. use Dijkstra’s, repair as necessary

• Little concept of time

• Solved (OSPF,...)


Dynamic - connected

Route:Route:

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• Assume path exists, discover as required

• Mostly ignore time, think only over short intervals

• Working protocols (AODV, DYMO,...)


Dynamic - not nec. connected

• Source to destination path does not necessarily exist!

• Network is constantly changing

• Not well understood

• Not well solved


Specific questions

• How to route through a dynamic network?

• What are the baseline performance limits?

• What information can be used?

• What are the practical performance limits?

• How to extend concepts from static graphs to dynamic graphs?


DTN protocols

• Direct delivery:

➡ No routing, wait until source node meets the destination node. Poor delivery and latency

• First contact:

➡ Forward the message to the first contact, i.e. message follows a random walk. Same issues.

• Epidemic:

➡ Replicate the packet at every contact. Optimal route? Poor performance due to congestion.

8


DTN protocols

• Spray and Wait

➡ Limit total number of copies. Either one or remainder/2 to contact. Explore n paths.

• PRoPHET:

➡ Maintain history of contacts, swap history on contact, also data if better chances. Next hop estimation.

• MaxProp:

➡ Use history to estimate path likelihood. Acks.


Time-expanded graph

• Determine the discrete states the graph exists in and expand them out explicitly

• Simplified communication model

• Feasible for sparse dynamic graphs


Time-expanded - construction

time

space


Yellow Cabs in SF

• All ~500 taxi cabs in the San Francisco Yellow Cab fleet

• Taxis log and send GPS position and occupancy state once a minute

• One month dataset available on Crawdad, continuous data available over web api


Yellow Cabs in SF


Contacts few and far between

5 min. 30 min. 1 1.5 2 4time (hours)

1 0 -2

1 0 -1

1 0 0

inter-contacttimeCCDF

10% of inter-contact times <30 minutes

90% of inter-contact times < 2 12 hours

14

multi-hop DTN is essential


SF Taxi TE-Graph

• 500 taxis moving for two hours

• Time-expanded graph is large but manageable

➡ 150,000 nodes

➡ ~2 million edges

➡ 1.5 GB (can be optimized)

➡ 2 minutes to construct, 10’s of seconds for shortest path


20 taxis over 2 hours

16


Delivery with finite window

06:00:

00

08:00:

00

10:00:

00

0.0

0.2

0.4

0.6

0.8

1.0

deliveryrate

4 hours

4hours

1 min.

5 min.

30 min.

1 hr.

4 hr.

17


Traffic patterns

• Base is the taxi depot (280/C. Chavez)

• Collection (position updates):

➡ All taxis periodically send updates to the base

➡ Period 5 - 10 minutes

• Peer-to-base (occ. status updates):

➡ Taxis randomly decide to send to the base station

• Peer-to-peer (random messages?):


Delivery probability

40%

60%

80%

100%

Peer2peer Peer2base Collection

Del

iver

y P

rob

abil

ity

(%

)

DD FC E SW P MP Opt

0%

20%

19


Routing overhead

100

150

200

250

300

350

Peer2peer Peer2base Collection

Ov

erh

ead

Rat

io

DD FC E SW P MP Opt

0

50

20


Delivery latency

Peer2base Collection

Av

erag

e L

aten

cy (

sec)

DD FC E SW P MP Opt

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

Peer2peer

21


Network structure

• Understand the graph structure

➡ Extend ideas from static network analysis

• Betweenness centrality

➡ trajectory betweenness informs about the importance of that trajectory in the network, remove that agent and what happens.

➡ edge betweenness gives the importance of a communication opportunity, indicates the contention that would occur if everyone tried to do uncapacitated shortest path routing


Network structure

• Closeness centrality

➡ Closeness centrality:: propagation delay for information spread, new method to reflect the routing protocol behavior?


Network measures

how hard is it to get knowledge?

how useful is knowledge?

static

rwp

dtn

SFdieselnet

infocom


Context/broader impact

• Improved understanding of dynamic communication networks

• New routing protocol/adaptation

• Wider applicability to other dynamic networks

➡ social networks (email, news propagation)

➡ animal networks

➡ p2p networks


communication in dynamic networkssing.stanford.edu/slides/ian-dynamic.pdf · communication in...

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