Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page
Demo Data from VLDB 2026 / reviewers in the wild / expert

Duke Harlan

dblp:33/9447 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
0since 2021 · last 2011
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 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 · 44% Energy-efficient computing · 44% Storage systems · 13%
Databases, data mining, and information retrieval
1 paper
Distributed and cloud data management · 100%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Distributed and cloud data management
data replication		 0.112011
Scarlett: coping with skewed content popularity in mapreduce clusters · EuroSys 2011
Cloud and datacenter computing
cluster resource management and scheduling		 0.112011
Scarlett: coping with skewed content popularity in mapreduce clusters · EuroSys 2011
Energy-efficient computing › thermal management
hot-spot mitigation		 0.112011
Scarlett: coping with skewed content popularity in mapreduce clusters · EuroSys 2011

Methods — techniques the papers use, named apart from their topics

trace-driven simulation · 0.2
YearPublicationVenuePosition
2011 Scarlett: coping with skewed content popularity in mapreduce clusters
abstract
To improve data availability and resilience MapReduce frameworks use file systems that replicate data uniformly. However, analysis of job logs from a large production cluster shows wide disparity in data popularity. Machines and racks storing popular content become bottlenecks; thereby increasing the completion times of jobs accessing this data even when there are machines with spare cycles in the cluster. To address this problem, we present Scarlett, a system that replicates blocks based on their popularity. By accurately predicting file popularity and working within hard bounds on additional storage, Scarlett causes minimal interference to running jobs. Trace driven simulations and experiments in two popular MapReduce frameworks (Hadoop, Dryad) show that Scarlett effectively alleviates hotspots and can speed up jobs by 20.2%.
Ganesh Ananthanarayanan, Sameer Agarwal 0002, Srikanth Kandula, Albert G. Greenberg, Ion Stoica, Duke Harlan
 EuroSys6