EDBT 2026 Demo / reviewers in the wild / expert
Ramaswamy Aditya
dblp:271/5953
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 1
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 |
Network management and operations · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Cloud and datacenter computing
datacenter operations |
0.4 | 1 | 2020 | Scouts: Improving the Diagnosis Process Through Domain-customized Incident Routing · SIGCOMM 2020 |
Network management and operations › fault management
fault diagnosis |
0.1 | 1 | 2020 | Scouts: Improving the Diagnosis Process Through Domain-customized Incident Routing · SIGCOMM 2020 |
Methods — techniques the papers use, named apart from their topics
machine learning · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | Scouts: Improving the Diagnosis Process Through Domain-customized Incident RoutingabstractIncident routing is critical for maintaining service level objectives in the cloud: the time-to-diagnosis can increase by 10x due to mis-routings. Properly routing incidents is challenging because of the complexity of today's data center (DC) applications and their dependencies. For instance, an application running on a VM might rely on a functioning host-server, remote-storage service, and virtual and physical network components. It is hard for any one team, rule-based system, or even machine learning solution to fully learn the complexity and solve the incident routing problem. We propose a different approach using per-team Scouts. Each teams' Scout acts as its gate-keeper --- it routes relevant incidents to the team and routes-away unrelated ones. We solve the problem through a collection of these Scouts. Our PhyNet Scout alone --- currently deployed in production --- reduces the time-to-mitigation of 65% of mis-routed incidents in our dataset. Nofel Yaseen, Robert MacDavid, Felipe Vieira Frujeri, Vincent Liu 0001, Ricardo Bianchini, Ramaswamy Aditya, Xiaohang Wang 0008, Henry Lee, David A. Maltz, Minlan Yu, Behnaz Arzani |
SIGCOMM | 7 |