Demo Data from EDBT 2026 / reviewers in the wild / expert
Ashraf Abdel-Hadi
dblp:279/5606
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2024
0009-0002-2579-2852ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 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% | |
| Computer networks
1 paper |
Software-defined and programmable networks · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Cloud and datacenter computing
cluster resource management and scheduling |
0.8 | 1 | 2024 | Draconis: Network-Accelerated Scheduling for Microsecond-Scale Workloads · EuroSys 2024 |
Cloud and datacenter computing › cluster resource management and scheduling
cluster scheduling |
0.8 | 1 | 2024 | Draconis: Network-Accelerated Scheduling for Microsecond-Scale Workloads · EuroSys 2024 |
Software-defined and programmable networks
programmable data plane |
0.2 | 1 | 2024 | Draconis: Network-Accelerated Scheduling for Microsecond-Scale Workloads · EuroSys 2024 |
Methods — techniques the papers use, named apart from their topics
programmable switch · 1.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Draconis: Network-Accelerated Scheduling for Microsecond-Scale WorkloadsabstractWe present Draconis, a novel scheduler for workloads in the range of tens to hundreds of microseconds. Draconis challenges the popular belief that programmable switches cannot house the complex data structures, such as queues, needed to support an in-network scheduler. Using programmable switches, Draconis achieves the low scheduling tail latency and high throughput needed to support these microsecond-scale workloads on large clusters. Furthermore, Draconis supports a wide range of complex scheduling policies, including locality-aware scheduling, priority-based scheduling, and resource-based scheduling. Sreeharsha Udayashankar, Ashraf Abdel-Hadi, Ali José Mashtizadeh, Samer Al-Kiswany |
EuroSys | 2 |