MespaConfig: Memory-Sparing Configuration Auto-Tuning for Co-Located In-Memory Cluster Computing Jobs

Distributed in-memory computing frameworks usually have lots of parameters (e.g., the buffer size of shuffle) to form a configuration for each execution. A well-tuned configuration can bring large improvements of performance. However, to improve resource utilization, jobs are often share the same cl...

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Vydané v:	IEEE transactions on services computing Ročník 15; číslo 5; s. 2883 - 2896
Hlavní autori:	Zong, Zan, Wen, Lijie, Hu, Xuming, Han, Rui, Qian, Chen, Lin, Li
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.09.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Cluster computing Clusters co-locate Computation Computational modeling Configuration tuning Configurations Distributed memory in-memory computing Load modeling memory-sparing Optimization Parallel processing Performance enhancement performance optimization Predictive models Resource utilization Tuning
ISSN:	1939-1374, 2372-0204
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