Improving Data Locality of Tasks by Executor Allocation in Spark Computing Environment

The concept of data locality is crucial for distributed systems (e.g., Spark and Hadoop) to process Big Data. Most of the existing research optimized the data locality from the aspect of task scheduling. However, as the execution container of Spark's tasks, the executor launched on different no...

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Veröffentlicht in:	IEEE transactions on cloud computing Jg. 12; H. 3; S. 876 - 888
Hauptverfasser:	Fu, Zhongming, He, Mengsi, Yi, Yang, Tang, Zhuo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.07.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Access time Algorithms Benchmarks Big Data Communication Communications traffic Costs Data locality Distributed databases distributed systems executor allocation Heuristic methods Performance evaluation Resource management Scheduling Scheduling algorithms Spark framework Sparks Task analysis Task scheduling
ISSN:	2168-7161, 2372-0018
Online-Zugang:	Volltext
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