Szczegóły publikacji
Opis bibliograficzny
Evaluating the use of policy gradient optimization approach for automatic cloud resource provisioning / Włodzimierz FUNIKA, Paweł Koperek // W: Parallel Processing and Applied Mathematics : 13th international conference, PPAM 2019 : Białystok, Poland, September 8–11, 2019 : revised selected papers, Pt. 1 / eds. Roman Wyrzykowski [et al.]. — Cham : Springer Nature Switzerland, cop. 2020. — (Lecture Notes in Computer Science ; ISSN 0302-9743 ; LNCS 12043. Theoretical Computer Science and General Issues ; ISSN 0302-9743). — ISBN: 978-3-030-43228-7; e-ISBN: 978-3-030-43229-4. — S. 467–478. — Bibliogr. s. 477–478, Abstr. — Publikacja dostępna online od: 2020-03-19
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 128263 |
|---|---|
| Data dodania do BaDAP | 2020-04-20 |
| Tekst źródłowy | URL |
| DOI | 10.1007/978-3-030-43229-4_40 |
| Rok publikacji | 2020 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | International Conference on Parallel Processing and Applied Mathematics 2019 |
| Czasopisma/serie | Lecture Notes in Computer Science, Theoretical Computer Science and General Issues |
Abstract
Reinforcement learning is a very active field of research with many practical applications. Success in many cases is driven by combining it with Deep Learning. In this paper we present results of our attempt to use modern advancements in this area for automated management of resources used to host distributed software. We describe the use of three policy training algorithms from the policy gradient optimization family, to create a policy used to control the behavior of an autonomous management agent. The agent is interacting with a simulated cloud computing environment, which is processing a stream of computing jobs. We discuss and compare the policy performance aspects and the feasibility to use them in real-world scenarios.
