Demo Data from EDBT 2026 / reviewers in the wild / expert
Ben Galewsky
dblp:286/8463
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 2021 · last 2022
0000-0003-3811-5949ORCID · 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 2021Applied, interdisciplinary, general and emerging computing · 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 · 61% Distributed systems · 30% High-performance computing · 9% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Distributed systems › distributed database
federated systems |
0.6 | 1 | 2022 | $f$funcX: Federated Function as a Service for Science · IEEE Trans. Parallel Distributed Syst. 2022 |
Cloud and datacenter computing › serverless computing
function-as-a-service |
0.6 | 1 | 2022 | $f$funcX: Federated Function as a Service for Science · IEEE Trans. Parallel Distributed Syst. 2022 |
Cloud and datacenter computing
serverless computing |
0.6 | 1 | 2022 | $f$funcX: Federated Function as a Service for Science · IEEE Trans. Parallel Distributed Syst. 2022 |
High-performance computing
scientific computing systems |
0.2 | 1 | 2022 | $f$funcX: Federated Function as a Service for Science · IEEE Trans. Parallel Distributed Syst. 2022 |
Methods — techniques the papers use, named apart from their topics
in-memory data store · 0.6container warming-aware routing · 0.6container management · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | $f$funcX: Federated Function as a Service for ScienceabstractƒuncX is a distributed function as a service (FaaS) platform that enables flexible, scalable, and high performance remote function execution. Unlike centralized FaaS systems, ƒuncX decouples the cloud-hosted management functionality from the edge-hosted execution functionality. ƒuncX's endpoint software can be deployed, by users or administrators, on arbitrary laptops, clouds, clusters, and supercomputers, in effect turning them into function serving systems. ƒuncX's cloud-hosted service provides a single location for registering, sharing, and managing both functions and endpoints. It allows for transparent, secure, and reliable function execution across the federated ecosystem of endpoints—enabling users to route functions to endpoints based on specific needs. ƒuncX uses containers (e.g., Docker, Singularity, and Shifter) to provide common execution environments across endpoints. ƒuncX implements various container management strategies to execute functions with high performance and efficiency on diverse ƒuncX endpoints. ƒuncX also integrates with an in-memory data store and Globus for managing data that may span endpoints. We motivate the need for ƒuncX, present our prototype design and implementation, and demonstrate, via experiments on two supercomputers, that ƒuncX can scale to more than 130000 concurrent workers. We show that ƒuncX's container warming-aware routing algorithm can reduce the completion time for 3,000 functions by up to 61% compared to a randomized algorithm and the in-memory data store can speed up data transfers by up to 3x compared to a shared file system. Zhuozhao Li, Ryan Chard, Yadu N. Babuji, Ben Galewsky, Tyler J. Skluzacek, Kirill Nagaitsev, Anna Woodard, Ben Blaiszik, Josh Bryan, Daniel S. Katz, Ian T. Foster, Kyle Chard |
IEEE Trans. Parallel Distributed Syst. | 4 |
| 2021 | Federated Function as a Service for eScienceabstractThe function as a service paradigm aims to abstract the complexities of managing computing infrastructure for users. While adoption in industry has been swift, we have yet to see widespread adoption in academia. This is in part due to barriers such as the need to access large research data, diverse hardware requirements, monolithic code bases, and existing systems available to researchers. We describe funcX, a federated functionas-a-service platform that addresses important requirements for use of FaaS in research computing. We outline how funcX has been used in early science deployments. Yadu N. Babuji, Josh Bryan, Ryan Chard, Kyle Chard, Ian T. Foster, Ben Galewsky, Daniel S. Katz, Zhuozhao Li |
e-Science | 6 |