Avatars Grow Legs: Generating Smooth Human Motion from Sparse Tracking Inputs with Diffusion Model
Yuming Du, Robin Kips, Albert Pumarola, Sebastian Starke, Ali Thabet, Artsiom Sanakoyeu
USENIX Conference on File Storage Technologies (FAST)
We present a multilayer study of the Facebook Messages stack, which is based on HBase and HDFS. We collect and analyze HDFS traces to identify potential improvements, which we then evaluate via simulation. Messages represents a new HDFS workload: whereas HDFS was built to store very large files and receive mostly- sequential I/O, 90% of files are smaller than 15MB and I/O is highly random. We find hot data is too large to easily fit in RAM and cold data is too large to easily fit in flash; however, cost simulations show that adding a small flash tier improves performance more than equivalent spending on RAM or disks. HBase’s layered design offers simplicity, but in trade for performance; our simulations show that network I/O can be halved if compaction bypasses the replication layer. Finally, although Messages is read-dominated, several features of the stack (i.e., logging, compaction, replication, and caching) amplify write I/O, causing writes to dominate disk I/O.
Yuming Du, Robin Kips, Albert Pumarola, Sebastian Starke, Ali Thabet, Artsiom Sanakoyeu
Bilge Acun, Benjamin Lee, Fiodar Kazhamiaka, Kiwan Maeng, Manoj Chakkaravarthy, Udit Gupta, David Brooks, Carole-Jean Wu
Harjasleen Malvai, Lefteris Kokoris-Kogias, Alberto Sonnino, Esha Ghosh, Ercan Ozturk, Kevin Lewi, Sean Lawlor