Self-adaptive Executors for Big Data Processing

Dataset

Description

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.
Date made available6 Sept 2019
PublisherTU Delft - 4TU.ResearchData
Date of data production2018 - 2019
  • 1 Conference contribution

  • Self-adaptive Executors for Big Data Processing

    Omranian Khorasani, S., Rellermeyer, J. S. & Epema, D., 13 Sept 2019, Middleware 2019 - Proceedings of the 2019 20th International Middleware Conference: Proceedings of the 20th International Middleware Conference. New York: Association for Computing Machinery (ACM), p. 176-188 13 p. (Middleware 2019 - Proceedings of the 2019 20th International Middleware Conference).

    Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

    Open Access
    File
    4 Citations (Scopus)
    320 Downloads (Pure)

Cite this