Name
Abstract | ||
|---|---|---|
A major challenge in edge computing platforms for container-based applications is dealing with the dynamic workload. At certain times, the resources previously allocated to a given container may not be adequate for a substantial increase in processing requests. This paper proposes an online machine learning auto-scaling approach for applications running at the network edge. The auto-scaling follow... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/DASC-PICom-CBDCom-CyberSciTech52372.2021.00038 | 2021 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) |
Keywords | DocType | ISBN |
Training,Machine learning,Quality of service,Computer architecture,Predictive models,Containers,Big Data | Conference | 978-1-6654-2174-4 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thiago Pereira da Silva | 1 | 0 | 0.34 |
| Aluízio F. Rocha Neto | 2 | 0 | 0.34 |
| Thais Vasconcelos Batista | 3 | 0 | 0.34 |
| Frederico A. S. Lopes | 4 | 0 | 0.34 |
| Flávia C. Delicato | 5 | 0 | 0.34 |
| Paulo F. Pires | 6 | 0 | 0.34 |