Quality of cloud services determined by the dynamic management of scheduling models for complex heterogeneous workloads
Authors:
- Damián Fernández-Cerero,
- Alejandro Fernández-Montes,
- Joanna Kołodziej,
- Laurent Lefèvre
Abstract
The quality of services in Cloud Computing (CC) depends on the scheduling strategies selected for processing of the complex workloads in the physical cloud clusters. Using the scheduler of the single type does not guarantee of the optimal mapping of jobs onto cloud resources, especially in the case of the processing of the big data workloads. In this paper, we compare the performances of the cloud schedulers for various combinations of the cloud workloads with different characteristics. We define several scenarios where the proper types of schedulers can be selected from a list of scheduling models implemented in the system, and used to schedule the concrete workloads based on the workloads' parameters and the feedback on the efficiency of the schedulers. The presented work is the first step in the development and implementation of an automatic intelligent scheduler selection system. In our simple experimental analysis, we confirm the usefulness of such a system in today's data-intensive cloud computing
- Record ID
- CUT978271d0392c494b94f4a35f2289eb44
- Publication categories
- ; ;
- Author
- Pages
- 210-219
- Other elements of collation
- schem.; tab.; wykr.; Bibliografia (na s.) - 217-219; Bibliografia (liczba pozycji) - 41; Oznaczenie streszczenia - Abstr.
- Book
- QUATIC 2018 : 2018 International Conference on the Quality of Information and Communications Technology, Coimbra, Portugal 4-7 September 2018 : proceedings, 2018, [S.l.], Institute of Electrical and Electronics Engineers, IEEE, ISBN 978-1-5386-5841-3 electronic
- Keywords in English
- big data, quality of big data, scheduling, cloud scheduling, dynamic cloud scheduling
- DOI
- DOI:10.1109/QUATIC.2018.00039 Opening in a new tab
- URL
- https://ieeexplore.ieee.org/document/8590192 Opening in a new tab
- Language
- eng (en) English
- Score (nominal)
- 20
- Additional fields
- Indeksowana w: Web of Science, Scopus, CORE
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUT978271d0392c494b94f4a35f2289eb44/
- URN
urn:pkr-prod:CUT978271d0392c494b94f4a35f2289eb44
* presented citation count is obtained through Internet information analysis, and it is close to the number calculated by the Publish or PerishOpening in a new tab system.