Seth Pollen

dblp:330/8107 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2023
0000-0001-8758-2522ORCID · reported

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

Systems, architecture and hardware · 2 · 2 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 · 80% Cloud and datacenter computing · 15% Performance modeling and evaluation · 5%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Memory systems › cache management › cache insertion policy
cache admission
1.222023
CacheSack: Theory and Experience of Google's Admission Optimization for Datacenter Flash Caches · ACM Trans. Storage 2023
CacheSack: Admission Optimization for Google Datacenter Flash Caches · USENIX ATC 2022
Memory systems › cache management › storage caching
flash cache
1.222023
CacheSack: Theory and Experience of Google's Admission Optimization for Datacenter Flash Caches · ACM Trans. Storage 2023
CacheSack: Admission Optimization for Google Datacenter Flash Caches · USENIX ATC 2022
Memory systems
cache management
0.612022
CacheSack: Admission Optimization for Google Datacenter Flash Caches · USENIX ATC 2022
Cloud and datacenter computing
datacenter storage
0.612022
CacheSack: Admission Optimization for Google Datacenter Flash Caches · USENIX ATC 2022

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

workload partitioning · 0.7knapsack formulation · 0.7optimization · 0.6cache admission · 0.6
YearPublicationVenuePosition
2023 CacheSack: Theory and Experience of Google's Admission Optimization for Datacenter Flash Caches
abstract
This article describes the algorithm, implementation, and deployment experience of CacheSack, the admission algorithm for Google datacenter flash caches. CacheSack minimizes the dominant costs of Google’s datacenter flash caches: disk IO and flash footprint. CacheSack partitions cache traffic into disjoint categories, analyzes the observed cache benefit of each subset, and formulates a knapsack problem to assign the optimal admission policy to each subset. Prior to this work, Google datacenter flash cache admission policies were optimized manually, with most caches using the Lazy Adaptive Replacement Cache algorithm. Production experiments showed that CacheSack significantly outperforms the prior static admission policies for a 7.7% improvement of the total cost of ownership, as well as significant improvements in disk reads (9.5% reduction) and flash wearout (17.8% reduction).
Tzu-Wei Yang, Seth Pollen, Mustafa Uysal, Arif Merchant, Homer Wolfmeister, Junaid Khalid
ACM Trans. Storage2
2022 CacheSack: Admission Optimization for Google Datacenter Flash Caches
Tzu-Wei Yang, Seth Pollen, Mustafa Uysal, Arif Merchant, Homer Wolfmeister
USENIX ATC2