Demo Data from EDBT 2026 / reviewers in the wild / expert
Jeremy Hemmo
dblp:295/3634
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2021
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, 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
1 paper |
Cloud and datacenter computing · 100% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Cloud and datacenter computing › multi-tenancy
tenant isolation |
0.1 | 1 | 2021 | QuiCK: A Queuing System in CloudKit · SIGMOD Conference 2021 |
Methods — techniques the papers use, named apart from their topics
two-level sharding · 0.5FoundationDB Record Layer · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | QuiCK: A Queuing System in CloudKitabstractWe present QuiCK, a queuing system built for managing asynchronous tasks in CloudKit, Apple's storage backend service. QuiCK stores queued messages along with user data in CloudKit, and supports CloudKit's tenancy model including isolation, fair resource allocation, observability, and tenant migration. QuiCK is built on the FoundationDB Record Layer, an open source transactional DBMS. It employs massive two-level sharding, with tens of billions of queues on the first level (separately storing the queued items for each user of every CloudKit app), and hundreds of queues on a second level (one per FoundationDB cluster used by CloudKit). Our evaluation demonstrates that QuiCK scales linearly with additional consumer resources, effectively avoids contention, provides fairness across CloudKit tenants, and executes deferred tasks with low latency. Kfir Lev-Ari, Yizuo Tian, Alexander Shraer, Chris Douglas, Andrey Andreev, Kevin Beranek, Scott Dugas, Alec Grieser, Jeremy Hemmo |
SIGMOD Conference | 10 |