Szczegóły publikacji
Opis bibliograficzny
Data-driven adaptive prediction of cloud resource usage / Piotr NAWROCKI, Patryk OSYPANKA, Beata Posłuszny // Journal of Grid Computing ; ISSN 1570-7873. — 2023 — vol. 21 iss. 1 art. no. 6, s. 1-19. — Bibliogr. s. 18-19, Abstr. — Publikacja dostępna online od: 2023-01-03
Autorzy (3)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 144454 |
|---|---|
| Data dodania do BaDAP | 2023-01-12 |
| Tekst źródłowy | URL |
| DOI | 10.1007/s10723-022-09641-y |
| Rok publikacji | 2023 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Czasopismo/seria | Journal of Grid Computing |
Abstract
Predicting computing resource usage in any system allows optimized management of resources. As cloud computing is gaining popularity, the urgency of accurate prediction is reduced as resources can be scaled on demand. However, this may result in excessive costs, and therefore there is a considerable body of work devoted to cloud resource optimization which can significantly reduce the costs of cloud computing. The most promising methods employ load prediction and resource scaling based on forecast values. However, prediction quality depends on prediction method selection, as different load characteristics require different forecasting mechanisms. This paper presents a novel approach that incorporates data-driven adaptation of prediction algorithms to generate short- and long-term cloud resource usage predictions and enables the proposed solution to readjust to different load characteristics as well as both temporary and permanent usage changes. First, preliminary tests were performed that yielded promising results – up to 36% better prediction quality. Subsequently, a fully autonomous, multi-stage optimization solution was proposed. The proposed approach was evaluated using real-life historical data from various production servers. Experiment results demonstrate 9.28% to 80.68% better prediction quality when compared to static algorithm selection.
