Automated Hyperparameter Tuning for Adaptive Cloud Workload Prediction
Author:
Affiliation:
1. Computing Science, Umeå University, Umeå, SE
2. Intel Corporation, Intel Corporation, Neubiberg, DE
Funder
Financial support has been provided in part by the Knut and Alice Wallenberg Foundation under grant KAW 2019.0352 and by the eSSENCE Programme under the Swedish Government?s Strategic Research Initiative.
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3603166.3632244
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4. Yuming Cheng, Chao Wang, Huihuang Yu, Yahui Hu, and Xuehai Zhou. 2019. GRU-ES: Resource Usage Prediction of Cloud Workloads Using a Novel Hybrid Method. 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS) (2019), 1249--1256.
5. Deep Learning-Based Autoscaling Using Bidirectional Long Short-Term Memory for Kubernetes
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