Symbolic execution-driven extraction of the parallel execution plans of Spark applications-Reference-Cited by-同舟云学术

Symbolic execution-driven extraction of the parallel execution plans of Spark applications

Published:2019-08-12 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering
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Author:

Baresi Luciano¹,Denaro Giovanni²,Quattrocchi Giovanni¹

Affiliation:

1. Politecnico di Milano, Italy

2. University of Milano-Bicocca, Italy

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3338906.3338973

Reference34 articles.

1. 2017. Dynamic Resource Allocation in Spark. https://spark.apache.org/docs/ latest/job-scheduling.html. 2017. Dynamic Resource Allocation in Spark. https://spark.apache.org/docs/ latest/job-scheduling.html.

2. BlinkDB

3. Apache.org. 2019. RDD Programming Guide - Spark 2.4.0 documentation. https: //spark.apache.org/docs/latest/rdd-programming-guide.html. Apache.org. 2019. RDD Programming Guide - Spark 2.4.0 documentation. https: //spark.apache.org/docs/latest/rdd-programming-guide.html.

4. L. Baresi S. Guinea A. Leva and G. Quattrocchi. 2018. Fine-grained Dynamic Resource Allocation for Big-Data Applications. Technical Report. http://hdl.handle. net/11311/1057275 L. Baresi S. Guinea A. Leva and G. Quattrocchi. 2018. Fine-grained Dynamic Resource Allocation for Big-Data Applications. Technical Report. http://hdl.handle. net/11311/1057275

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