Alexander Afanasyev Tutors: Seung-Hoon Lee, Uichin Lee Content Distribution in VANETs using Network Coding: Evaluation of the Generation Selection Algorithms March 2, 2009

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

CarTorrent: BitTorrent-like Cooperative Content Distribution in VANETs 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: CodeTorrent Web Server A file is divided into pieces  Network coding “effectively” mitigates the peer/piece selection problem and “improves” the performance! Any linearly independent coded packet is helpful 1 more?

Network Coding Problem Processing Overhead  Delay without O/H ◦ Small # of generations is a better choice ◦ Larger # of generations  more severe coupon collection problem Single Generation 5/10/50 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 14 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? Gen1Gen2Gen3Local: (my status) Global: (neighbor status) Gen1Gen2Gen3 Request to ??

Generation Selection Strategies Virtual “Global” Completeness Vector Local Min Gen 2 Local Max Gen 1 Neighbor Min Gen 4 Neighbor Max Gen 3 Global Min: Gen 4 Global Max: Gen 3 Random: Random Sequential: Gen 1

Simulation Setup  Communications ◦ b; 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 map

Downloading all generations in parallel Generation Progress Global Min Local Min Random Neighbor Min

Downloading all generations in parallel Overall Progress Neighbor-aware strategy improves at the beginning of downloading * 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 parallel Finishing times histogram

Downloading generations (semi-)sequentially Generation Progress Global Max Neighbor Max Local Max Sequential

Downloading generations (semi-) sequentially Overall Progress * 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 (semi-)sequentially Finishing times histogram

Parallel vs Sequential Downloading Overall progress of the best strategies 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 Downloading Finishing 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 Facts Rank checking No rank check Bool vector Bool vector Int vector Int vector

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