CAVA: Exploring Memory Locality for Big Data Analytics in Virtualized Clusters

Hwang, Eunji; Kim, Hyun‐Goo; Nam, Beomseok; Choi, Young-ri

doi:10.1109/ccgrid.2018.00017

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2022

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“…The Cache-Affinity and Virtualization Aware (CAVA) [15] algorithm was developed by Wang et al in 2018. The main idea of this technique is to consider a run-time priority for the MapReduce applications based on their cache affinities.…”

Section: Related Workmentioning

confidence: 99%

CLQLMRS: improving cache locality in MapReduce job scheduling using Q-learning

et al. 2022

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Scheduling of MapReduce jobs is an integral part of Hadoop and effective job scheduling has a direct impact on Hadoop performance. Data locality is one of the most important factors to be considered in order to improve efficiency, as it affects data transmission through the system. A number of researchers have suggested approaches for improving data locality, but few have considered cache locality. In this paper, we present a state-of-the-art job scheduler, CLQLMRS (Cache Locality with Q-Learning in MapReduce Scheduler) for improving both data locality and cache locality using reinforcement learning. The proposed algorithm is evaluated by various experiments in a heterogeneous environment. Experimental results show significantly decreased execution time compared with FIFO, Delay, and the Adaptive Cache Local scheduler.

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Section: Related Workmentioning

confidence: 99%