Szczegóły publikacji
Opis bibliograficzny
Adaptive resource planning for cloud-based services using machine learning / Piotr NAWROCKI, Mikołaj Grzywacz, Bartłomiej ŚNIEŻYŃSKI // Journal of Parallel and Distributed Computing ; ISSN 0743-7315. — 2021 — vol. 152, s. 88–97. — Bibliogr. s. 97, Abstr. — Publikacja dostępna online od: 2021-03-03
Autorzy (3)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 133117 |
|---|---|
| Data dodania do BaDAP | 2021-03-24 |
| Tekst źródłowy | URL |
| DOI | 10.1016/j.jpdc.2021.02.018 |
| Rok publikacji | 2021 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Czasopismo/seria | Journal of Parallel and Distributed Computing |
Abstract
The problem of using cloud computing resources for services is related to planning the amount of resources needed and their subsequent reservation. This problem occurs both on the side of the customer who tries to minimize the cost of the service and on the side of the cloud provider who wants to make the best use of existing infrastructure without introducing any modifications. In our article, we want to show how the problem of overestimating the utilization of resources for services which use cloud computing can be handled. Solving this problem will allow significant savings to be made by both the customer and the cloud infrastructure provider. The system we have developed demonstrates the considerable utility of machine learning methods when planning cloud resource reservation for network services. The models proposed, which use a multilayer perceptron, have yielded good results for both short- and long-term reservations.
