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-29d192262686Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
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.
- 2019-09-06 first online, published, posted
publisher4TU.Centre for Research Data
formatmedia types: application/zip, text/csv, text/plain
organizationsTU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Department of Software Technology