Design Tradeoffs for SSD Performance N. Agrawal, V. Prabhakaran, T. Wobber, John D. Davis, M. Manasse, R. Panigrahy. Microsoft Research, Silicon Valley, Usenix ’08, ATC, 2008 Thank You! 2019. 10. 21 Presentation by Lee, Jeyeon 2reenact@dankook.ac.kr

Introduction Background SSD Basics Evaluation Conclusion

4 issues relevant to SSD performance 1. Introduction 4 issues relevant to SSD performance Data placement placement of data is critical to provide load balancing and wear-leveling. Parallelism memory components must be coordinated so as to operate in parallel. Write ordering small, ramdomly-ordered writes are tricky Workload management This presents a taxonomy of design with a trace-driven simulator extracted from real systems.

2. Background Flash Internals SSD Flash Package (4 GB)

Properties of Flash Memory 2. Background Properties of Flash Memory read : 25μs + 25ns/byte write : 100μs + 200μs erase : 1.5ms constraints : erase before reuse, erase limit(≈100K) Bandwidth and Interleaving there is bottleneck in SSD performance  “Interleaving” 32MB/sec  40MB/sec 13MB/sec  26MB/sec

3. SSD Basics Logical Block Map writes cannot be performed in place as on a rotating disk.  each write of a single LBA corresponds to a write of a different flash page.  SSD must maintain mapping between LBA and physical flash location. writes must be performed sequentially whenever possible. If a contiguous range of LBAs is mapped to the same physical die, performance for sequential access in large chunks will suffer. 1 2 3 4 5 6 7 1 2 3 4 5 6 7 LBA FTL FTL 4 1 Physical Block 1 5 2 3 2 6 4 5 3 7 6 7

3. SSD Basics Cleaning When a page is written, previous mapped contents are now out-of-date. We need a garbage collector to allocate fresh blocks. overprovisioning

Parallelism and Interconnect Density 3. SSD Basics Parallelism and Interconnect Density To achieve bandwidths, I/O requests are should handled in parallel. Parallel requests Ganging Interleaving Background cleaning Persistence Flash memory is by definition persistent storage. Rebuilding the logical block map and all related data structures is essential. Error detection and correction must be provided by application firmware.

Wear-leveling Greedy Rate limiting Migration 3. SSD Basics Wear-leveling Greedy recycle when a candidate’s remaining lifetime exceeds the threshold. Rate limiting rate-limit worn out blocks’ usage Migration migrate cold data into old blocks < Block Wear in IOzone > < Lifetime distribution >

Microbench, Interleaving 4. Evaluation Microbench, Interleaving Ganging Performance

5. Conclusions The NAND-flash based SSD is certain to represent a sea change in the architecture of computer storage subsystems. Many of the issues that arise in SSD design appear to mimic problems that have previously appeared in the storage stack. Trace-driven simulator and workload traces extracted from real systems will presents a taxonomy of such design choices.

Design Tradeoffs for SSD Performance N. Agrawal, V. Prabhakaran, T. Wobber, John D. Davis, M. Manasse, R. Panigrahy. Microsoft Research, Silicon Valley, Usenix ’08, ATC, 2008 Thank You! 2019. 10. 21 Presentation by Lee, Jeyeon 2reenact@dankook.ac.kr