This paper identifies, quantifies and demonstrates memory elasticity, an intrinsic property of data-parallel tasks.
Memory elasticity allows tasks to run with significantly less memory than they would ideally need while only paying a moderate performance penalty. We show that memory elasticity is prevalent in the Hadoop, Spark, Tez and Flink frameworks.
We also show that memory elasticity is predictable in nature by building simple models for Hadoop and extending them to Tez and Spark. To demonstrate the potential benefits of leveraging memory elasticity, this paper further explores its application to cluster scheduling.
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In this setting, we observe that the resource vs. Tasks may complete faster when scheduled with less memory because their waiting time is reduced.
We show that a scheduler can turn this task-level tradeoff into improved job completion time and cluster-wide memory utilization. This internationally acclaimed show authentically revives the sublime harmonies of New Jersey's finest.
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