Skyrise: Exploiting Serverless Cloud Infrastructure for Elastic Data Processing

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Abstract

Serverless computing offers elasticity unmatched by conventional server-based cloud infrastructure. Although modern data processing systems embrace serverless storage, such as Amazon S3, they continue to manage their compute resources as servers. This is challenging for unpredictable workloads, leaving clusters often underutilized. Recent research shows the potential of serverless compute resources, such as cloud functions, for elastic data processing, but also sees limitations in performance robustness and cost efficiency for long running workloads. These challenges require holistic approaches across the system stack. However, to the best of our knowledge, there is no end-to-end data processing system built entirely on serverless infrastructure. In this paper, we present Skyrise, our effort towards building the first fully serverless SQL query processor. Skyrise exploits the elasticity of its underlying infrastructure, while alleviating the inherent limitations with a number of adaptive and cost-aware techniques. We show that both Skyrise’s performance and cost are competitive to other cloud data systems for terabyte-scale queries of the analytical TPC‑H benchmark.

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【要点】：论文提出了Skyrise，一种完全基于无服务器基础设施构建的SQL查询处理器，通过自适应和成本感知技术克服无服务器计算在性能和成本效率方面的限制。

【方法】：Skyrise采用了一系列自适应和成本感知技术，以利用无服务器基础设施的弹性，并优化性能与成本。

【实验】：作者在Skyrise上运行了TPC-H基准测试的查询，结果显示其性能和成本与其他云数据系统相当，实验使用的数据集为TPC-H数据集。

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