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Jimmy Lu

dblp:34/320 · DBLP profile ↗
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4ranked-venue papers
0as first author
3since 2021 · last 2024
0000-0002-0560-9160ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 2 · 2 since 2021Software engineering, systems software and programming languages · 2 · 1 since 2021Databases, data management, data science and information retrieval · 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
4 papers
Memory systems · 75% Storage systems · 21% Cloud and datacenter computing · 4%

Topics — the 5 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Memory systems › cache management › storage caching
flash cache
1.832024
Baleen: ML Admission & Prefetching for Flash Caches · FAST 2024
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
0.512021
Kangaroo: Caching Billions of Tiny Objects on Flash · SOSP 2021
Memory systems
cache
0.412020
The CacheLib Caching Engine: Design and Experiences at Scale · OSDI 2020
Memory systems › cache
DRAM cache
0.112021
Kangaroo: Caching Billions of Tiny Objects on Flash · SOSP 2021
Cloud and datacenter computing
datacenter storage
0.112020
The CacheLib Caching Engine: Design and Experiences at Scale · OSDI 2020

Methods — techniques the papers use, named apart from their topics

machine learning · 0.8write-optimized caching · 0.5DRAM-optimized caching · 0.5
YearPublicationVenuePosition
2024 Baleen: ML Admission & Prefetching for Flash Caches
Daniel Lin-Kit Wong, Carson Molder, Sathya Gunasekar, Jimmy Lu, Snehal Khandkar, Daniel S. Berger, Nathan Beckmann, Gregory R. Ganger
FAST5
2022 Kangaroo: Theory and Practice of Caching Billions of Tiny Objects on Flash
abstract
Many 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. Storage6
2021 Kangaroo: Caching Billions of Tiny Objects on Flash
abstract
Many 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
SOSP6
2020 The CacheLib Caching Engine: Design and Experiences at Scale
Benjamin Berg, Daniel S. Berger, Sara McAllister, Isaac Grosof, Sathya Gunasekar, Jimmy Lu, Michael Uhlar, Jim Carrig, Nathan Beckmann, Mor Harchol-Balter, Gregory R. Ganger
OSDI6