Self-adaptive Executors for Big Data Processing

Datacite citation style:
Omranian Khorasani, S. (Sobhan) (2019): Self-adaptive Executors for Big Data Processing. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/uuid:38529ffe-00d0-42b0-9b3c-29d192262686
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Dataset
Delft University of Technology logo
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This dataset contains the measurements obtained with Apache Spark using different strategies for adapting the number of executor threads to reduce I/O contention. The two main strategies explored are a static solution (number of executor threads for I/O intensive tasks pre-determined) and a dynamic solution that employs an active control loop to measure epoll_wait time.
history
  • 2019-09-06 first online, published, posted
publisher
4TU.Centre for Research Data
format
media types: application/zip, text/csv, text/plain
organizations
TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Department of Software Technology
contributors
  • Epema, D.H.J. (Dick) orcid logo
  • Rellermeyer, J.S. (Jan) orcid logo

DATA

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