Resource management of cloud-enabled systems using model-free reinforcement learning
| Autor: | Makram Bouzid, Armen Aghasaryan, Dimitre Kostadinov, Yue Jin |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry Control (management) 020206 networking & telecommunications Cloud computing 02 engineering and technology Field (computer science) Value of information System model Human–computer interaction 0202 electrical engineering electronic engineering information engineering Reinforcement learning 020201 artificial intelligence & image processing Resource management Electrical and Electronic Engineering business 5G |
| Zdroj: | Annals of Telecommunications. 74:625-636 |
| ISSN: | 1958-9395 0003-4347 |
| DOI: | 10.1007/s12243-019-00720-y |
| Popis: | The digital system of the future will face the growing challenge of controlling the system behavior in complex dynamically evolving environments. In this paper, we examine the applicability of a new management paradigm based on a reinforcement learning approach, where no preliminary specification of the system model is required. The learning agent identifies the most adequate control policies in live interaction with a partially observed system and provides it with autonomous management capabilities. We present the results of experimentation with cloud-based applications and discuss the technical challenges that need to be addressed in this field. Furthermore, we present the results of experimentation on a 5G network slice that hosts a cloud-based application in a multi-agent reinforcement learning setting, and demonstrate the value of information exchange between learning agents. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink