Cost-effective Resource Provisioning for Spark Workloads

Yuxing Chen, Jiaheng Lu, Chen Chen, Mohammad Ashraful Hoque, Sasu Tarkoma

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


Spark is one of the prevalent big data analytical platforms. Configuring proper resource provision for Spark jobs is challenging but essential for organizations to save time, achieve high resource utilization, and remain cost-effective. In this paper, we study the challenge of determining the proper parameter values that meet the performance requirements of workloads while minimizing both resource cost and resource utilization time. We propose a simulation-based cost model to predict the performance of jobs accurately. We achieve low-cost training by taking advantage of simulation framework, i.e., Monte Carlo (MC) simulation, which uses a small amount of data and resources to make a reliable prediction for larger datasets and clusters. The salient feature of our method is that it allows us to invest low training cost while obtaining an accurate prediction. Through experiments with six benchmark workloads, we demonstrate that the cost model yields less than 7% error on average prediction accuracy and the recommendation achieves up to 5x resource cost saving.
Original languageEnglish
Title of host publicationCIKM '19 : Proceedings of the 28th ACM International Conference on Information and Knowledge Management
Number of pages4
Place of PublicationNew York, NY
Publication date3 Nov 2019
ISBN (Print)978-1-4503-6976-3
Publication statusPublished - 3 Nov 2019
MoE publication typeA4 Article in conference proceedings
EventACM International Conference on Information and Knowledge Management - Beijing, China
Duration: 3 Nov 20197 Nov 2019
Conference number: 28

Fields of Science

  • 113 Computer and information sciences

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