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

Usage statistics

994
views
348
downloads

Licence

CC0
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

Files (2)