DISCERNER: Dynamic selection of resource manager in hyper-scale cloud-computing data centres
Authors:
- Damián Fernández-Cerero,
- F. Javier Ortega,
- Agnieszka Jakóbik,
- Alejandro Fernández-Montes
Abstract
Data centres constitute the engine of the Internet, and run a major portion of large web and mobile applications, content delivery and sharing platforms, and Cloud-computing business models. The high performance of such infrastructures is therefore critical for their correct functioning. This work focuses on the improvement of data-centre performance by dynamically switching the main data-centre governance software system: the resource manager. Instead of focusing on the development of new resource-managing models as soon as new workloads and patterns appear, we propose DISCERNER, a decision-theory model that can learn from numerous data-centre execution logs to determine which existing resource-managing model may optimise the overall performance for a given time period. Such a decision-theory system employs a classic machine-learning classifier to make real-time decisions based on past execution logs and on the current data-centre operational situation. A set of extensive and industry-guided experiments has been simulated by a validated data-centre simulation tool. The results obtained show that the values of key performance indicators may be improved by at least 20% in realistic scenarios.
- Record ID
- CUTe45b2a87e443457ab6b8c55088cf187b
- Publication categories
- ;
- Author
- Journal series
- Future Generation Computer Systems, ISSN 0167-739X, e-ISSN 1872-7115
- Issue year
- 2021
- Vol
- 116
- Pages
- 190-199
- Other elements of collation
- schem.; tab.; wykr.; Bibliografia (na s.) - 198-199; Bibliografia (liczba pozycji) - 32; Oznaczenie streszczenia - Abstr.; Numeracja w czasopiśmie - Vol. 116
- Substantive notes
- Artykuł zawiera: Supplementary material
- Keywords in English
- data centre, decision theory, machine learning, Cloud computing
- DOI
- DOI:10.1016/j.future.2020.10.031 Opening in a new tab
- URL
- http://www.sciencedirect.com/science/article/pii/S0167739X20330156 Opening in a new tab
- Language
- eng (en) English
- Score (nominal)
- 140
- Additional fields
- Indeksowana w: Scopus
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUTe45b2a87e443457ab6b8c55088cf187b/
- URN
urn:pkr-prod:CUTe45b2a87e443457ab6b8c55088cf187b
* 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.