Szczegóły publikacji
Opis bibliograficzny
Signature-based adaptive cloud resource usage prediction using machine learning and anomaly detection / Wiktor SUS, Piotr NAWROCKI // Journal of Grid Computing ; ISSN 1570-7873. — 2024 — vol. 22 iss. 2 art. no. 46, s. 1–15. — Bibliogr. s. 14–15, Abstr. — Publikacja dostępna online od: 2024-04-23
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 152873 |
|---|---|
| Data dodania do BaDAP | 2024-05-07 |
| Tekst źródłowy | URL |
| DOI | 10.1007/s10723-024-09764-4 |
| Rok publikacji | 2024 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Journal of Grid Computing |
Abstract
One of the challenges in managing cloud computing clusters is assigning resources based on the customers’ needs. For this mechanism to work efficiently, it is imperative that there are sufficient resources reserved to maintain continuous operation, but not too much to avoid overhead costs. Additionally, to avoid the overhead of acquisition time, it is important to reserve resources sufficiently in advance. This paper presents a novel reliable general-purpose mechanism for prediction-based resource usage reservation. The proposed solution should be capable of operating for long periods of time without drift-related problems, and dynamically adapt to changes in system usage. To achieve this, a novel signature-based ensemble prediction method is presented, which utilizes multiple distinct prediction algorithms suited for various use-cases, as well as an anomaly detection mechanism used to improve prediction accuracy. This ensures that the mechanism can operate efficiently in different real-life scenarios. Thanks to a novel signature-based selection algorithm, it is possible to use the best available prediction algorithm for each use-case, even over long periods of time, which would typically lead to drifts. The proposed approach has been evaluated using real-life historical data from various production servers, which include traces from more than 1,500 machines collected over more than a year. Experimental results have demonstrated an increase in prediction accuracy of up to 21.4 percent over the neural network approach. The evaluation of the proposed approach highlights the importance of choosing the appropriate prediction method, especially in diverse scenarios where the load changes frequently.
