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

Dataset

• Distributed Computing
• Information Systems

Apache Spark, Middleware 2019

CC0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by S. (Sobhan) Omranian Khorasani

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

4TU.Centre for Research Data

media types: application/zip, text/csv, text/plain

TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Department of Software Technology

• Epema, D.H.J. (Dick)
• Rellermeyer, J.S. (Jan)