Bayesian Inference Classification Deep Learning Distributed Computing Federated Learning Inverse Problem Model Identification Optimization Robust Learning Sampling Software Surrogate Modeling Unsupervised Learning 

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                    Aerts S Ait-Mlouk A Akesson M Alawadi S Braet Q Cheng L Chu J Claeys T Costa M F Couckuyt I Coulier A Deschrijver D Dhaene T Drawert B Ekmefjord M Elamin M M Elsayed K Enberg R Engblom S Eriksson R Ferranti F Fronk C Geiger M Gong X Hast A Hellander Avan der Herten J Hoebeke J Jacob B Javed O Jiang R Jiang R M Joseph W Ju L Karakulev A Koay Y S Martens L Matthew S Moretti S others  Petzold L Petzold L R Petzold. L Pissoort D Plets D Reger G Reger G Rogier H Rossi M Rumsey B Singh P Spjuth O Sturrock M Tanghe E Toor S Trogh J Vandenbosch G A Vats E Vermeeren G Waltari H Wrede F Yi T Yun J Zachariah D
                
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 Chu J;  Singh P;  Toor S
Efficient Resource Scheduling for Distributed Infrastructures Using Negotiation Capabilities Conference 
2023 IEEE 16th International Conference on Cloud Computing (CLOUD), IEEE IEEE, 2023.
Abstract | Links | BibTeX | Tags: Deep Learning, Distributed Computing, Surrogate Modeling
@conference{nokey,

title = {Efficient Resource Scheduling for Distributed Infrastructures Using Negotiation Capabilities},

author = {Junjie Chu and Prashant Singh and Salman Toor},

url = {https://ieeexplore.ieee.org/abstract/document/10255003},

doi = {https://doi.org/10.1109/CLOUD60044.2023.00065},

year  = {2023},

date = {2023-07-02},

urldate = {2023-07-02},

booktitle = {2023 IEEE 16th International Conference on Cloud Computing (CLOUD)},

publisher = {IEEE},

organization = {IEEE},

abstract = {The information explosion drives enterprises and individuals to rent cloud computing infrastructure for their applications in the cloud. However, the agreements between cloud computing providers and clients are often inefficient. We propose an agent-based auto-negotiation system for resource scheduling using fuzzy logic. Our method completes a one-to-one auto-negotiation process and generates optimal offers for providers and clients. We compare the impact of different member functions, fuzzy rule sets, and negotiation scenarios on the offers to optimize the system. Our proposed method efficiently utilizes resources and offers interpretability, high flexibility, and customization. We successfully train machine learning models to replace the fuzzy negotiation system, improving processing speed. The article also highlights potential future improvements to the proposed system and machine learning models.},

keywords = {Deep Learning, Distributed Computing, Surrogate Modeling},

pubstate = {published},

tppubtype = {conference}

}

Close
The information explosion drives enterprises and individuals to rent cloud computing infrastructure for their applications in the cloud. However, the agreements between cloud computing providers and clients are often inefficient. We propose an agent-based auto-negotiation system for resource scheduling using fuzzy logic. Our method completes a one-to-one auto-negotiation process and generates optimal offers for providers and clients. We compare the impact of different member functions, fuzzy rule sets, and negotiation scenarios on the offers to optimize the system. Our proposed method efficiently utilizes resources and offers interpretability, high flexibility, and customization. We successfully train machine learning models to replace the fuzzy negotiation system, improving processing speed. The article also highlights potential future improvements to the proposed system and machine learning models.
Close
https://ieeexplore.ieee.org/abstract/document/10255003
doi:https://doi.org/10.1109/CLOUD60044.2023.00065
Close
 Ju L;  Singh P;  Toor S
Proactive autoscaling for edge computing systems with kubernetes Proceedings Article 
In: Proceedings of the 14th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 1–8, 2021.
Links | BibTeX | Tags: Distributed Computing
@inproceedings{ju2021proactive,

title = {Proactive autoscaling for edge computing systems with kubernetes},

author = {Li Ju and Prashant Singh and Salman Toor},

url = {https://dl.acm.org/doi/abs/10.1145/3492323.3495588},

doi = {https://doi.org/10.1145/3492323.3495588},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

booktitle = {Proceedings of the 14th IEEE/ACM International Conference on Utility and Cloud Computing Companion},

pages = {1--8},

keywords = {Distributed Computing},

pubstate = {published},

tppubtype = {inproceedings}

}

Close
https://dl.acm.org/doi/abs/10.1145/3492323.3495588
doi:https://doi.org/10.1145/3492323.3495588
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 Singh P;  Elamin M M;  Toor S
Towards Smart e-Infrastructures, A Community Driven Approach Based on Real Datasets Proceedings Article 
In: 2020 IEEE Green Technologies Conference (GreenTech), pp. 109–114, IEEE 2020.
Links | BibTeX | Tags: Deep Learning, Distributed Computing, Surrogate Modeling
@inproceedings{singh2020towards,

title = {Towards Smart e-Infrastructures, A Community Driven Approach Based on Real Datasets},

author = {Prashant Singh and Mona Mohamed Elamin and Salman Toor},

url = {https://ieeexplore.ieee.org/abstract/document/9289758

https://arxiv.org/pdf/2012.09579

},

doi = {https://doi.org/10.1109/GreenTech46478.2020.9289758},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

booktitle = {2020 IEEE Green Technologies Conference (GreenTech)},

pages = {109--114},

organization = {IEEE},

keywords = {Deep Learning, Distributed Computing, Surrogate Modeling},

pubstate = {published},

tppubtype = {inproceedings}

}

Close
https://ieeexplore.ieee.org/abstract/document/9289758
https://arxiv.org/pdf/2012.09579
doi:https://doi.org/10.1109/GreenTech46478.2020.9289758
Close