Demo Data from VLDB 2026 / reviewers in the wild / expert
Angela Schörgendorfer
dblp:125/8095
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2014
—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 networks
1 paper |
Network optimization and economics · 100% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Storage systems · 100% |
Topics — the 1 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Storage systems › storage management › backup and recovery
data backup |
0.1 | 1 | 2014 | Distributed backup scheduling: Modeling and optimization · INFOCOM 2014 |
Methods — techniques the papers use, named apart from their topics
stochastic modeling · 0.4simulation · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2014 | Distributed backup scheduling: Modeling and optimizationabstractRecent years have seen rapid growth in data storage, magnifying the importance of ensuring data safety by performing regular backups. However, traffic created by such backups can be a significant burden on the underlying communication network. In the present paper we address the tradeoff between frequent backups (increased safety) and reducing the network peak load. We address the problem of shifting backup traffic from peak hours to off-peak hours within the constraints imposed by user connectivity. Backups are scheduled using a distributed protocol characterized by a set of probabilities that indicate the likelihood of a user initiating a backup during a given hour. Given these probabilities, we study the network capacity by investigating the rate at which users can generate data while retaining stable backlog processes. We then derive explicit expressions for the stationary behavior of the backup process, and discuss how to choose the backup probabilities that strike the right balance between a low peak load and data safety. Via simulation experiments we show that this approach is highly successful in reducing costs. Peter M. van de Ven, Angela Schörgendorfer |
INFOCOM | 3 |