Alexander Afanasyev

Tutors: Seung-Hoon Lee, Uichin Lee

Content Content Distribution in VANETs using Network Distribution in VANETs using Network Coding: Coding:

Evaluation of the Generation Selection Evaluation of the Generation Selection AlgorithmsAlgorithms

March 2, 2009

Applications ◦ Software updates and patches (e.g., navigation map, games)◦ Multimedia data downloads (e.g., videos, news, etc.)

Content Distribution in Content Distribution in Vehicular Ad-Hoc Networks (VANETs)Vehicular Ad-Hoc Networks (VANETs)

◦ High mobility (i.e., highly dynamic networks)◦ Error-prone channel (due to obstacles, multi-path fading, etc.)

Content Distribution Challenges Content Distribution Challenges

CarTorrent: BitTorrent-like CarTorrent: BitTorrent-like Cooperative Content Distribution in Cooperative Content Distribution in VANETsVANETs

Web Server

A file is divided into pieces

Exchange pieces via Vehicle-to-Vehicle Communications

Download a file (piece by piece)

Problem: Peer & Piece selection coupon collection problem

Cannot complete download!

Not useful!

Using Network Coding: Using Network Coding: CodeTorrentCodeTorrent

Web Server

A file is divided into pieces

Any linearly independent coded packet is helpful

1 more?

Network Coding ProblemProcessing Overhead

Delay without O/H◦ Small # of generations is a better choice◦ Larger # of generations more severe coupon collection problem

Single Generation

Single Generation

5/10/50 Generations5/10/50 Generations

Overhead

Solution: divide a file into small generations◦ Problem: too many generation causes a coupon

collection problem◦ Conflicting goals: maximizing benefits of NC vs.

minimizing coding O/H

Mitigating Coding Overheads

50MB

Solution: divide a file into small generations◦ Problem: too many generation causes a coupon

collection problem◦ Conflicting goals: maximizing benefits of NC vs.

minimizing coding O/H

Mitigating Coding Overheads

1 4

10MB x 5

What is optimal strategy for generation downloading?

Checking neighbor rank improve chances of linearly independent block, but◦ Low-rank cars can also have valuable blocks

Back to the BitTorrent problem of piece/generation selection◦ Local status based decision (i.e., the least/the most

downloaded generation, sequential order)?◦ Neighbor status based decision?◦ Random?

Gen1 Gen2 Gen3Local:(my status)

Global:(neighbor status)

Gen1 Gen2 Gen3

Request to ??

Generation Selection Generation Selection StrategiesStrategies

Virtual “Global” Completeness Vector

Lo

cal Min

Gen

2

Lo

cal Max

Gen

1

Neig

hb

or M

in

Gen

4

Neig

hb

or M

ax

Gen

3Global Min: Gen 4Global Max: Gen 3Random: RandomSequential: Gen 1

Simulation Setup Communications

◦ 802.11b; 11Mbps + Two-ray ground propagation Mobility

◦ Random Waypoint model w/ speed range of [0,20] m/s◦ Westwood area map: 2400m*2400m

Nodes◦ 3 APs: file sources◦ 200 nodes/40% interest level:

80 nodes are downloading a file Download parameters

◦ 50 megabyte file◦ 10 generations

Westwood area mapWestwood area map

Downloading all generations in Downloading all generations in parallelparallelGeneration ProgressGeneration Progress

Global MinGlobal Min Local MinLocal Min

RandomRandomNeighbor Min

Neighbor Min

Downloading all generations in Downloading all generations in parallelparallelOverall ProgressOverall Progress

Neighbor-aware strategy improves at the beginning of

downloading

* confidence interval is calculated with probability 95% using 8 simulations

* confidence interval is calculated with probability 95% using 8 simulations

Local-aware and random strategies

has smaller tail

Conclusions: Network-aware strategy has long tail of finishing times Local and random strategies behave almost as good as global status-aware

Downloading all generations in Downloading all generations in parallelparallelFinishing times histogramFinishing times histogram

Downloading generations Downloading generations (semi-)sequentially(semi-)sequentiallyGeneration ProgressGeneration Progress

Global Max

Global Max

Neighbor Max

Neighbor Max

Local MaxLocal Max

SequentialSequential

Downloading generations Downloading generations (semi-)(semi-)sequentiallysequentiallyOverall ProgressOverall Progress

* confidence interval is calculated with probability 95% using 8 simulations

* confidence interval is calculated with probability 95% using 8 simulations

!!! Neighbor-aware strategy

outperforms local and global one

Conclusions: Network-aware strategy outperforms other strategies Average finishing time for global/local max strategies 1.5 times worse

than neighborhood status aware policy

Downloading generations Downloading generations (semi-)sequentially(semi-)sequentiallyFinishing times histogramFinishing times histogram

Parallel vs Sequential DownloadingParallel vs Sequential DownloadingOverall progress of the best Overall progress of the best strategiesstrategies

Conclusions: Neighbor-aware generation choosing considerably improves chances for

helpful block (linearly independent) at the beginning Local or random strategy improves download finishing time

Parallel vs Sequential DownloadingParallel vs Sequential DownloadingFinishing times histogramFinishing times histogram

Conclusions: Neighbor-aware strategies have on average 20% worse finishing times

than local max strategy

Checking generation rank of the available generation greatly improves performance for neighbor status aware strategies

Integer vector gossiping decrease overall download performance

Interesting FactsInteresting FactsRank

checkingRank

checking

No rank check

No rank check

BoolvectorBool

vector

Intvector

Intvector

Conclusion Generation selection strategy in multi-generation

CodeTorrent downloads have big impact on the overall download performance

Local status aware strategies (local-min, random) have the best finishing performance

Neighbor status aware strategies have the best start-up performance

It is important to check rank for neighbor status aware strategies

Future work◦ Investigate performance of combined strategies◦ Check performance using different node mobility models