1. 2019
  2. Dataset: Self-adaptive Executors for Big Data Processing

    Omranian Khorasani, S., Rellermeyer, J. S. & Epema, D., 13 Sep 2019

    Research output: Non-textual formData set/DatabaseScientific

  3. Self-adaptive Executors for Big Data Processing

    Omranian Khorasani, S., Rellermeyer, J. S. & Epema, D., 13 Sep 2019, (Accepted/In press) Proceedings of the 20th ACM/IFIP International Middleware Conference. 13 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

  4. Fair multiple-workflow scheduling with different quality-of-service goals

    Rezaeian, A., Naghibzadeh, M. & Epema, D. H. J., 2019, In : Journal of Supercomputing. 75, 2, p. 746-769 24 p.

    Research output: Contribution to journalArticleScientificpeer-review

  5. 2018
  6. An Experimental Performance Evaluation of Autoscalers for Complex Workflows

    Ilyushkin, A., Ali-Eldin, A., Herbst, N., Bauer, A., Papadopoulos, A., Epema, D. & Iosup, A., Apr 2018, In : ACM Transactions on Modeling and Performance Evaluation of Computing Systems (TOMPECS). 3, 2, p. 1-32 32 p., 8.

    Research output: Contribution to journalArticleScientificpeer-review

  7. Achieving Performance Balance among Spark Frameworks with Two-Level Schedulers

    Kuzmanovska, A., van den Bogert, H., Mak, R. & Epema, D., 2018, Proceedings - 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2018. Los Alamitos, CA: IEEE Computer Society, p. 133-142 10 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

  8. The Impact of Task Runtime Estimate Accuracy on Scheduling Workloads of Workflows

    Ilyushkin, A. & Epema, D., 2018, 18th IEEE/ACM Int'l Symp. on Cluster, Cloud and Grid Computing. p. 331-341 11 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

  9. 2017
  10. A Coflow-based Co-optimization Framework for High-performance Data Analytics

    Cheng, L., Wang, Y., Pei, Y. & Epema, D., 2017, Proceedings - 46th International Conference on Parallel Processing, ICPP 2017. Los Alamitos, CA: IEEE Computer Society, p. 392-401 10 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

  11. An Experimental Performance Evaluation of Autoscaling Policies for Complex Workflows

    Ilyushkin, A., Ali-Eldin, A., Herbst, N., Papadopoulos, A., Ghit, B., Epema, D. & Iosup, A., 2017, Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering, ICPE 2017. New York, NY: Association for Computing Machinery (ACM), p. 75-86 12 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

  12. Better Safe than Sorry: Grappling with Failures of In-Memory Data Analytics Frameworks

    Ghit, B. & Epema, D., 2017, Proceedings of the 26th International Symposium on High-Performance Parallel and Distributed Computing, HPDC 2017. New York, NY: Association for Computing Machinery (ACM), p. 105-116 12 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

  13. Modeling, Analysis, and Experimental Comparison of Streaming Graph-Partitioning Policies

    Guo, Y., Hong, S., Chafi, H., Iosup, A. & Epema, D., 2017, In : Journal of Parallel and Distributed Computing. 108, p. 106-121 16 p.

    Research output: Contribution to journalArticleScientificpeer-review

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