"False"
Umeå University
printicon
Main menu hidden.
Staff photo Lidia Kidane

Lidia Kidane

Contact

E-mail
lidia.kidane@umu.se
Phone

Works as

Affiliation
Affiliated as research student at Department of Computing Science
Location
MIT-huset, Umeå universitet, MIT.D.246 Umeå universitet, 901 87 Umeå
Accurate and low-overhead workload prediction for cloud management
Report / UMINF, 25.09
Kidane, Lidia
CloudResilienceML: ensuring robustness of machine learning models in dynamic cloud systems
Proceedings - 2024 IEEE/ACM 17th International Conference on Utility and Cloud Computing, UCC 2024, IEEE 2024 : 73-81
Nguyen, Chanh; Kidane, Lidia; Duy, Vo Nguyen Le; et al.
CloudResilienceML: ensuring robustness of machine learning models in dynamic cloud systems
The 17th IEEE/ACM International Conference on Utility and Cloud Computing (UCC 2024)
Nguyen, Chanh Le Tan; Kidane, Lidia; Vo Nguyen Le, Duy; et al.
Automated hyperparameter tuning for adaptive cloud workload prediction
UCC '23: Proceedings of the IEEE/ACM 16th International Conference on Utility and Cloud Computing
Kidane, Lidia; Townend, Paul; Metsch, Thijs; et al.
When and How to Retrain Machine Learning-based Cloud Management Systems
2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), IEEE 2022 : 688-698
Kidane, Lidia; Townend, Paul; Metsch, Thijs; et al.
Efficient retraining of machine learning algorithms in cloud management systems
Kidane, Lidia; Townend, Paul; Metsch, Thijs; et al.
A hybrid autoencoder-LSTM framework for efficient workload prediction
Kidane, Lidia; Townend, Paul; Metsch, Thijs; et al.
A data-driven framework for efficient and automated workload prediction in cloud computing
Kidane, Lidia; Townend, Paul; Metsch, Thijs; et al.
View publications in DiVA

Research groups

Group member
Autonomous Distributed Systems Lab