Demo Data from VLDB 2026 / reviewers in the wild / expert
Julian Tutuncu-Macias
dblp:304/2327
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 2021 · last 2022
0000-0003-4286-7721ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1 · 1 since 2021Software engineering, systems software and programming languages · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer architecture, parallel and distributed computing, and storage systems
2 papers |
Memory systems · 65% Storage systems · 35% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Memory systems › cache management › storage caching
flash cache |
1.1 | 2 | 2022 | Kangaroo: Theory and Practice of Caching Billions of Tiny Objects on Flash · ACM Trans. Storage 2022 Kangaroo: Caching Billions of Tiny Objects on Flash · SOSP 2021 |
Storage systems
flash and SSD |
0.5 | 1 | 2021 | Kangaroo: Caching Billions of Tiny Objects on Flash · SOSP 2021 |
Memory systems › cache
DRAM cache |
0.1 | 1 | 2021 | Kangaroo: Caching Billions of Tiny Objects on Flash · SOSP 2021 |
Methods — techniques the papers use, named apart from their topics
write-optimized caching · 0.5DRAM-optimized caching · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Kangaroo: Theory and Practice of Caching Billions of Tiny Objects on FlashabstractMany social-media and IoT services have very large working sets consisting of billions of tiny (≈100 B) objects. Large, flash-based caches are important to serving these working sets at acceptable monetary cost. However, caching tiny objects on flash is challenging for two reasons: (i) SSDs can read/write data only in multi-KB “pages” that are much larger than a single object, stressing the limited number of times flash can be written; and (ii) very few bits per cached object can be kept in DRAM without losing flash’s cost advantage. Unfortunately, existing flash-cache designs fall short of addressing these challenges: write-optimized designs require too much DRAM, and DRAM-optimized designs require too many flash writes. We present Kangaroo , a new flash-cache design that optimizes both DRAM usage and flash writes to maximize cache performance while minimizing cost. Kangaroo combines a large, set-associative cache with a small, log-structured cache. The set-associative cache requires minimal DRAM, while the log-structured cache minimizes Kangaroo’s flash writes. Experiments using traces from Meta and Twitter show that Kangaroo achieves DRAM usage close to the best prior DRAM-optimized design, flash writes close to the best prior write-optimized design, and miss ratios better than both. Kangaroo’s design is Pareto-optimal across a range of allowed write rates, DRAM sizes, and flash sizes, reducing misses by 29% over the state of the art. These results are corroborated by analytical models presented herein and with a test deployment of Kangaroo in a production flash cache at Meta. Sara McAllister, Benjamin Berg, Julian Tutuncu-Macias, Juncheng Yang, Sathya Gunasekar, Jimmy Lu, Daniel S. Berger, Nathan Beckmann, Gregory R. Ganger |
ACM Trans. Storage | 3 |
| 2021 | Kangaroo: Caching Billions of Tiny Objects on FlashabstractMany social-media and IoT services have very large working sets consisting of billions of tiny (≈100 B) objects. Large, flash-based caches are important to serving these working sets at acceptable monetary cost. However, caching tiny objects on flash is challenging for two reasons: (i) SSDs can read/write data only in multi-KB "pages" that are much larger than a single object, stressing the limited number of times flash can be written; and (ii) very few bits per cached object can be kept in DRAM without losing flash's cost advantage. Unfortunately, existing flash-cache designs fall short of addressing these challenges: write-optimized designs require too much DRAM, and DRAM-optimized designs require too many flash writes. Sara McAllister, Benjamin Berg, Julian Tutuncu-Macias, Juncheng Yang, Sathya Gunasekar, Jimmy Lu, Daniel S. Berger, Nathan Beckmann, Gregory R. Ganger |
SOSP | 3 |