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Manuel Schröder

dblp:217/3854 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Network management and operations › fault management
fault diagnosis
0.412019
Zooming in on wide-area latencies to a global cloud provider · SIGCOMM 2019
Network management and operations › fault management › fault diagnosis
fault localization
0.412019
Zooming in on wide-area latencies to a global cloud provider · SIGCOMM 2019
Network management and operations › fault management › fault diagnosis
passive diagnosis
0.412019
Zooming in on wide-area latencies to a global cloud provider · SIGCOMM 2019
Network measurement and analytics › web measurement
CDN measurement
0.312018
Odin: Microsoft's Scalable Fault-Tolerant CDN Measurement System · NSDI 2018
Network measurement and analytics › web performance measurement
CDN performance measurement
0.112018
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
YearPublicationVenuePosition
2019 Zooming in on wide-area latencies to a global cloud provider
abstract
The 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
SIGCOMM6
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
NSDI3