Lynceus: Cost-efficient Tuning and Provisioning of Data Analytic Jobs

@article{Casimiro2020LynceusCT,
  title={Lynceus: Cost-efficient Tuning and Provisioning of Data Analytic Jobs},
  author={Maria Casimiro and Diego Didona and P. Romano and L. Rodrigues and Willy Zwanepoel and D. Garlan},
  journal={2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)},
  year={2020},
  pages={56-66}
}
Modern data analytic and machine learning jobs find in the cloud a natural deployment platform to satisfy their notoriously large resource requirements. Yet, to achieve cost efficiency, it is crucial to identify a deployment configuration that satisfies user-defined QoS constraints (e.g., on execution time), while avoiding unnecessary over-provisioning.This paper introduces Lynceus, a new approach for the optimization of cloud-based data analytic jobs that improves over state-of-the-art… Expand
TrimTuner: Efficient Optimization of Machine Learning Jobs in the Cloud via Sub-Sampling
Finding the right cloud configuration for analytics clusters

References

SHOWING 1-10 OF 54 REFERENCES
Cumulon: optimizing statistical data analysis in the cloud
Arrow: Low-Level Augmented Bayesian Optimization for Finding the Best Cloud VM
Metis: Robustly Tuning Tail Latencies of Cloud Systems
...
1
2
3
4
5
...