Cost-effective Resource Provisioning for Spark Workloads
Author:
Affiliation:
1. University of Helsinki, Helsinki, Finland
2. Huawei Canada Research Centre, Toronto, Canada
Funder
Huawei HIRP open project
Crowdsourced Battery Optimization AI for a Connected World (CBAI)
Academy of Finland
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3357384.3358090
Reference14 articles.
1. Monte Carlo Simulations in Statistical Physics
2. CRESP: Towards Optimal Resource Provisioning for MapReduce Computing in Public Clouds
3. A Study of SQL-on-Hadoop Systems
4. Dynamic Configuration of Partitioning in Spark Applications
5. Using machine learning to optimize parallelism in big data applications
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1. Towards General and Efficient Online Tuning for Spark;Proceedings of the VLDB Endowment;2023-08
2. SimCost: cost-effective resource provision prediction and recommendation for spark workloads;Distributed and Parallel Databases;2023-06-22
3. KORDI: A Framework for Real-Time Performance and Cost Optimization of Apache Spark Streaming;2023 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS);2023-04
4. Mjolnir: A framework agnostic auto-tuning system with deep reinforcement learning;Applied Intelligence;2022-10-20
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