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Performance Improvement of DAG-Aware Task Scheduling Algorithms with Efficient Cache Management in Spark
2021
Electronics
Directed acyclic graph (DAG)-aware task scheduling algorithms have been studied extensively in recent years, and these algorithms have achieved significant performance improvements in data-parallel analytic platforms. However, current DAG-aware task scheduling algorithms, among which HEFT and GRAPHENE are notable, pay little attention to the cache management policy, which plays a vital role in in-memory data-parallel systems such as Spark. Cache management policies that are designed for Spark
doi:10.3390/electronics10161874
fatcat:6flphen445b4pb4uvfpoqqqjoi