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SMConf: One-Size-Fit-Bunch, Automated Memory Capacity Configuration for In-memory Data Analytic Platform
2021
Computers Materials & Continua
Spark is the most popular in-memory processing framework for big data analytics. Memory is the crucial resource for workloads to achieve performance acceleration on Spark. The extant memory capacity configuration approach in Spark is to statically configure the memory capacity for workloads based on user's specifications. However, without the deep knowledge of the workload's system-level characteristics, users in practice often conservatively overestimate the memory utilizations of their
doi:10.32604/cmc.2020.012513
fatcat:hspzysshtzel3djkq4jygt23wa