Cost-effective Resource Provisioning for Spark Workloads

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Sammanfattning

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.
Originalspråkengelska
Titel på gästpublikationCIKM '19 : Proceedings of the 28th ACM International Conference on Information and Knowledge Management
Antal sidor4
UtgivningsortNew York, NY
FörlagACM
Utgivningsdatum3 nov 2019
Sidor2477-2480
ISBN (tryckt)978-1-4503-6976-3
DOI
StatusPublicerad - 3 nov 2019
MoE-publikationstypA4 Artikel i en konferenspublikation
EvenemangACM International Conference on Information and Knowledge Management - Beijing, Kina
Varaktighet: 3 nov 20197 nov 2019
Konferensnummer: 28
http://www.cikm2019.net/

Vetenskapsgrenar

  • 113 Data- och informationsvetenskap

Citera det här

Chen, Y., Lu, J., Chen, C., Hoque, M. A., & Tarkoma, S. (2019). Cost-effective Resource Provisioning for Spark Workloads. I CIKM '19 : Proceedings of the 28th ACM International Conference on Information and Knowledge Management (s. 2477-2480). ACM. https://doi.org/10.1145/3357384.3358090