Szczegóły publikacji
Opis bibliograficzny
Continuous self-adaptation of control policies in automatic cloud management / Włodzimierz FUNIKA, Paweł Koperek, Jacek KITOWSKI // W: Euro-Par 2021: Paralel processing workshops : international workshops : Lisbon, Portugal, August 30–31, 2021 : revised selected papers / eds. Ricardo Chaves [et al.]. — Cham : Springer Nature Switzerland, cop. 2022. — (Lecture Notes in Computer Science ; ISSN 0302-9743 ; vol. 13098). — ISBN: 978-3-031-06155-4; e-ISBN: 978-3-031-06156-1. — S. 69–80. — Bibliogr., Abstr. — Publikacja dostępna online od: 2022-06-09
Autorzy (3)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 142980 |
|---|---|
| Data dodania do BaDAP | 2022-10-29 |
| DOI | 10.1007/978-3-031-06156-1_6 |
| Rok publikacji | 2022 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | International European Conference on Parallel and Distributed Computing |
| Czasopismo/seria | Lecture Notes in Computer Science |
Abstract
Deep Reinforcement Learning has been recently a very active field of research. The policies generated with use of that class of training algorithms are flexible and thus have many practical applications. In this paper we present the results of our attempt to use the recent advancements in Reinforcement Learning to automate the management of resources in a compute cloud environment. We describe a new approach to self-adaptation of autonomous management, which uses a digital clone of the managed infrastructure to continuously update the control policy. We present the architecture of our system and discuss the results of evaluation which includes autonomous management of a sample application deployed to Amazon Web Services cloud. We also provide the details of training of the management policy using the Proximal Policy Optimization algorithm. Finally, we discuss the feasibility to extend the presented approach to further scenarios.