~/talk

Discard-Based Garbage Collection for Distributed Log-Structured Storage Systems in...

on YouTube 17m
Talkat FAST '26 · 2026-02-24..02-26· [distributed-systems]

speakers

name:Runhua Bian
links:none on file
name:Liqiang Zhang
links:none on file
name:Jinxin Liu
links:none on file
name:Jiacheng Zhang
links:none on file
name:Jianong Zhong
links:none on file
name:Jiahao Gu
links:none on file
name:Hao Guo
links:none on file
name:Zhihong Guo
links:none on file
name:Yunhao Li
links:none on file
name:Fenghao Zhang
links:none on file
name:Jiangkun Zhao
links:none on file
name:Yangming Chen
links:none on file
name:Guojun Li
links:none on file
name:Ruwen Fan
links:none on file
name:Haijia Shen
links:none on file
name:Chengyu Dong
links:none on file
name:Yao Wang
links:none on file
name:Rui Shi
links:none on file
name:Jiwu Shu
links:none on file
name:Youyou Lu
bluesky:luyy.bsky.social

resources

video:https://youtu.be/M6mE2ua28mE
slides:none on file

summary

ByteStore implements a new Discard-and-Compaction GC called DisCoGC that reclaims large contiguous stale ranges without rewriting valid data, implemented by scanning block metadata, issuing batched discard requests into UFS, merging trims, and falling back to low-frequency compaction to defragment. Production deployment shows 32% lower logical write amplification, 10% lower space amplification and an overall 20% reduction in TCO with near-identical average latency and modest increase in physical writes.

AI-written summary · about

chapters (12)

  1. 0:00Disco GC introduction
  2. 2:29Traditional GC limitations
  3. 3:54Workload patterns from traces
  4. 5:00Discard GC mechanism
  5. 6:28Multi-layered storage challenges
  6. 8:57Metadata updates and contention
  7. 10:23Balancing discard and compaction
  8. 11:09Optimizing SSD trim operations
  9. 12:12Evaluation setup and workloads
  10. 13:17Performance results and TCO
  11. 14:25Lessons learned and recommendations
  12. 15:51Conclusion

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