Predicting Resource Requirement in Intermediate Palomar Transient Factory Workflow

Qiao Kang, Alex Sim, Peter Nugent, Sunwoo Lee, Wei-keng Liao, Ankit Agrawal, Alok Choudhary, Kesheng Wu

CCGrid, 2020

This paper proposes a Bayesian-network model that exploits the spatiotemporal correlation of astronomical images to predict per-stage resource requirements in transient-detection pipelines like iPTF, and shows it identifies the most influential spatial and temporal features and achieves prediction errors close to the intrinsic random-variance limit.

BibTeX

@inproceedings{kang2020predicting,
  title={Predicting Resource Requirement in Intermediate Palomar Transient Factory Workflow},
  author={Kang, Qiao and Sim, Alex and Nugent, Peter and Lee, Sunwoo and Liao, Wei-keng and Agrawal, Ankit and Choudhary, Alok and Wu, Kesheng},
  booktitle={2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID)},
  pages={619--628},
  year={2020},
  organization={IEEE}
}