@inproceedings{2aa1524ca387467a9e0db3bac3c657c4,
title = "Sparkaibench: a benchmark to generate ai workloads on spark",
abstract = "With the rapid development of artificial intelligence and cloud computing technologies, more and more workloads embed AI algorithms are deploying on cloud systems. For lack of sufficient achievements on generating AI workloads in recent years, designing and developing an efficient benchmark for AI workloads will be significant helpful for optimization of job execution time and cluster resource utilization. SparkAIBench proposed in this paper is a user customized benchmark and is able to automatically generate a variety of AI workloads by transforming user requirements into JSON objects. Besides, cooperated with a DRL-based job scheduling optimizer, a real scenario is introduced in this paper to demonstrate how SparkAIBench works.",
keywords = "AI workloads, Benchmark, Cloud computing",
author = "Zifeng Liu and Xiaojiang Zuo and Zeqing Li and Rui Han",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2020.; 2nd International Symposium on Benchmarking, Measuring, and Optimization, Bench 2019 ; Conference date: 14-11-2019 Through 16-11-2019",
year = "2020",
doi = "10.1007/978-3-030-49556-5_21",
language = "English",
isbn = "9783030495558",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "215--221",
editor = "Wanling Gao and Jianfeng Zhan and Geoffrey Fox and Xiaoyi Lu and Dan Stanzione",
booktitle = "Benchmarking, Measuring, and Optimizing - 2nd BenchCouncil International Symposium, Bench 2019, Revised Selected Papers",
address = "Germany",
}