Demo Data from SIGIR 2026 / reviewers in the wild / expert
Manuel Schröder
dblp:217/3854
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 2
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 networks
2 papers |
Network management and operations · 58% Network measurement and analytics · 42% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Cloud and datacenter computing · 100% |
Topics — the 5 heaviest of 7, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Network management and operations › fault management
fault diagnosis |
0.4 | 1 | 2019 | Zooming in on wide-area latencies to a global cloud provider · SIGCOMM 2019 |
Network management and operations › fault management › fault diagnosis
fault localization |
0.4 | 1 | 2019 | Zooming in on wide-area latencies to a global cloud provider · SIGCOMM 2019 |
Network management and operations › fault management › fault diagnosis
passive diagnosis |
0.4 | 1 | 2019 | Zooming in on wide-area latencies to a global cloud provider · SIGCOMM 2019 |
Network measurement and analytics › web measurement
CDN measurement |
0.3 | 1 | 2018 | Odin: Microsoft's Scalable Fault-Tolerant CDN Measurement System · NSDI 2018 |
Network measurement and analytics › web performance measurement
CDN performance measurement |
0.1 | 1 | 2018 | Odin: Microsoft's Scalable Fault-Tolerant CDN Measurement System · NSDI 2018 |
Methods — techniques the papers use, named apart from their topics
passive diagnosis · 0.8active probing · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | Zooming in on wide-area latencies to a global cloud providerabstractThe network communications between the cloud and the client have become the weak link for global cloud services that aim to provide low latency services to their clients. In this paper, we first characterize WAN latency from the viewpoint of a large cloud provider Azure, whose network edges serve hundreds of billions of TCP connections a day across hundreds of locations worldwide. In particular, we focus on instances of latency degradation and design a tool, BlameIt, that enables cloud operators to localize the cause (i.e., faulty AS) of such degradation. BlameIt uses passive diagnosis, using measurements of existing connections between clients and the cloud locations, to localize the cause to one of cloud, middle, or client segments. Then it invokes selective active probing (within a probing budget) to localize the cause more precisely. We validate BlameIt by comparing its automatic fault localization results with that arrived at by network engineers manually, and observe that BlameIt correctly localized the problem in all the 88 incidents. Further, BlameIt issues 72X fewer active probes than a solution relying on active probing alone, and is deployed in production at Azure. Sundararajan Renganathan, Ganesh Ananthanarayanan, Junchen Jiang, Venkat N. Padmanabhan, Manuel Schröder, Matt Calder, Arvind Krishnamurthy |
SIGCOMM | 6 |
| 2018 | Odin: Microsoft's Scalable Fault-Tolerant CDN Measurement System
Matt Calder, Ryan Gao, Manuel Schröder, Ryan Stewart, Jitendra Padhye, Ratul Mahajan, Ganesh Ananthanarayanan, Ethan Katz-Bassett |
NSDI | 3 |