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arXiv · 2207.02290

Blink: Lightweight Sample Runs for Cost Optimization of Big Data Applications

Hani Al-Sayeh·Muhammad Attahir Jibril·Bunjamin Memishi·Kai-Uwe Sattler
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Abstract

Distributed in-memory data processing engines accelerate iterative applications by caching substantial datasets in memory rather than recomputing them in each iteration. Selecting a suitable cluster size for caching these datasets plays an essential role in achieving optimal performance. In practice, this is a tedious and hard task for end users, who are typically not aware of cluster specifications, workload semantics and sizes of intermediate data. We present Blink, an autonomous sampling-based framework, which predicts sizes of cached datasets and selects optimal cluster size without relying on historical runs. We evaluate Blink on a variety of iterative, real-world, machine learning applications. With an average sample runs cost of 4.6% compared to the cost of optimal runs, Blink selects the optimal cluster size in 15 out of 16 cases, saving up to 47.4% of execution cost compared to average costs.

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Hani Al-Sayeh, Muhammad Attahir Jibril, Bunjamin Memishi, Kai-Uwe Sattler. 2022-07-05. Blink: Lightweight Sample Runs for Cost Optimization of Big Data Applications. https://arxiv.org/abs/2207.02290

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