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Distributed dataflow systems such as Apache Spark or Apache Flink enableparallel, in-memory data processing on large clusters of commodity hardware.Consequently, the appropriate amount of memory to allocate to the cluster is acrucial consideration.  In this paper, we analyze the challenge of efficient resource allocation fordistributed data processing, focusing on memory. We emphasize that in-memoryprocessing with in-memory data processing frameworks can undermine resourceefficiency. Based on the findings of our trace data analysis, we compilerequirements towards an automated solution for efficient cluster resourceallocation.

Selecting Efficient Cluster Resources for Data Analytics: When and How to Allocate for In-Memory Processing?