Cost-effective Resource Provisioning for Spark Workloads

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http://hdl.handle.net/10138/307832

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Chen , Y , Lu , J , Chen , C , Hoque , M A & Tarkoma , S 2019 , Cost-effective Resource Provisioning for Spark Workloads . in CIKM '19 : Proceedings of the 28th ACM International Conference on Information and Knowledge Management . ACM , New York, NY , pp. 2477-2480 , ACM International Conference on Information and Knowledge Management , Beijing , China , 03/11/2019 . https://doi.org/10.1145/3357384.3358090

Title: Cost-effective Resource Provisioning for Spark Workloads
Author: Chen, Yuxing; Lu, Jiaheng; Chen, Chen; Hoque, Mohammad Ashraful; Tarkoma, Sasu
Contributor: University of Helsinki, Department of Computer Science
University of Helsinki, Unified DataBase Management System research group / Jiaheng Lu
University of Helsinki, Department of Computer Science
University of Helsinki, Content-Centric Structures and Networking research group / Sasu Tarkoma
Publisher: ACM
Date: 2019-11-03
Language: eng
Number of pages: 4
Belongs to series: CIKM '19 Proceedings of the 28th ACM International Conference on Information and Knowledge Management
ISBN: 978-1-4503-6976-3
URI: http://hdl.handle.net/10138/307832
Abstract: 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.
Subject: 113 Computer and information sciences
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