@inproceedings{e9a1a3e0855648018c6981ec722ad5d8,
title = "WIRE: Resource-efficient Scaling with Online Prediction for DAG-based Workflows",
abstract = "This paper introduces WIRE that manages resources for the DAG-based workflows on IaaS clouds. WIRE predicts and plans resources over the MAPE (Monitor-AnalyzePlan-Execute) loops to: 1) Estimate task performance with online data, 2) Conduct simulations to predict the upcoming loads based on online estimates and workflow DAGs, 3) Apply a resource-steering policy to size cloud instance pools for the maximal parallelism that is consistent with low cost. We implement WIRE on Pegasus WMS/HTCondor and evaluate its performance on the ExoGENI network cloud. The results show that WIRE attains low resource cost with the performance that is typically within a factor of two of optimal.",
keywords = "Cloud computing, DAG-based workflows, Machine learning, Resource scaling",
author = "Bing Xie and Qiang Cao and Mayuresh Kunjir and Linli Wan and Jeff Chase and Anirban Mandal and Mats Rynge",
note = "Publisher Copyright: {\textcopyright}2021 IEEE.; 2021 IEEE International Conference on Cluster Computing, Cluster 2021 ; Conference date: 07-09-2021 Through 10-09-2021",
year = "2021",
doi = "10.1109/Cluster48925.2021.00025",
language = "英语",
series = "Proceedings - IEEE International Conference on Cluster Computing, ICCC",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "35--46",
booktitle = "Proceedings - 2021 IEEE International Conference on Cluster Computing, Cluster 2021",
address = "美国",
}