Distributed dataflow systems such as Apache Spark or Apache Flink enable parallel, in-memory data processing on large clusters of commodity hardware. Consequently, the appropriate amount of memory to allocate to the cluster is a crucial consideration. In this paper, we analyze the challenge of efficient resource allocation for distributed data processing, focusing on memory. We emphasize that in-memory processing with in-memory data processing frameworks can undermine resource efficiency. Based on the findings of our trace data analysis, we compile requirements towards an automated solution for efficient cluster resource allocation.

翻译：分布式数据流系统（如Apache Spark或Apache Flink）能够在商用硬件大规模集群上实现并行的内存数据处理。因此，为集群分配适当的内存资源是一个关键考量。本文分析了分布式数据处理中资源高效分配的挑战，重点关注内存方面。我们强调，使用内存数据处理框架进行内存处理可能会削弱资源效率。基于追踪数据分析的结果，我们整理了对自动化集群资源分配高效解决方案的需求。