Integrating OpenAI Gym and CloudSim Plus: A simulation environment for DRL Agent training in energy-driven cloud scaling.
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| Title: | Integrating OpenAI Gym and CloudSim Plus: A simulation environment for DRL Agent training in energy-driven cloud scaling. |
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| Authors: | Agos Jawaddi, Siti Nuraishah1 (AUTHOR) aishahagos96@gmail.com, Ismail, Azlan1,2 (AUTHOR) azlanismail@uitm.edu.my |
| Source: | Simulation Modelling Practice & Theory. Jan2024, Vol. 130, pN.PAG-N.PAG. 1p. |
| Subjects: | Deep reinforcement learning, Reinforcement learning, Research personnel, Ecology |
| Abstract: | Experimentation in real cloud environments for training Deep Reinforcement Learning (DRL) agents can be costly, time-consuming, and non-repeatable. To overcome these limitations, simulation-based approaches are promising alternatives. This paper introduces a specialized simulation environment that integrates OpenAI Gym, a popular platform for reinforcement learning, with CloudSim Plus, a versatile cloud simulation framework. The proposed simulator specifically focuses on the case study of energy-driven cloud scaling. By leveraging the strengths of both Python-based OpenAI Gym and Java-based CloudSim Plus, the simulation environment offers a flexible and extensible platform for DRL-Agent training. The integration is facilitated through a gateway that enables seamless interaction between the two frameworks. The simulation environment is designed to support the training process of DRL agents, enabling them to tackle the complexities of cloud scaling in an energy-aware context. It provides configurable settings that represent various cloud scaling scenarios, allowing researchers to explore different parameter configurations and evaluate the performance of DRL agents effectively. Through extensive experimentation, the proposed simulation environment demonstrates its functionality and applicability in measuring the performance of DRL agents with respect to energy-driven cloud scaling. The results obtained from the case study validate the effectiveness and potential of the simulation environment for training DRL agents in cloud scaling scenarios. Overall, this work presents a novel simulation environment that bridges the gap between DRL-Agent training and cloud scaling challenges, offering researchers a valuable tool for advancing the field of energy-driven cloud scaling through reinforcement learning. [ABSTRACT FROM AUTHOR] |
| Copyright of Simulation Modelling Practice & Theory is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 174060481 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Integrating OpenAI Gym and CloudSim Plus: A simulation environment for DRL Agent training in energy-driven cloud scaling. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Agos+Jawaddi%2C+Siti+Nuraishah%22">Agos Jawaddi, Siti Nuraishah</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> aishahagos96@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Ismail%2C+Azlan%22">Ismail, Azlan</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> azlanismail@uitm.edu.my</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Simulation+Modelling+Practice+%26+Theory%22">Simulation Modelling Practice & Theory</searchLink>. Jan2024, Vol. 130, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Deep+reinforcement+learning%22">Deep reinforcement learning</searchLink><br /><searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink><br /><searchLink fieldCode="DE" term="%22Research+personnel%22">Research personnel</searchLink><br /><searchLink fieldCode="DE" term="%22Ecology%22">Ecology</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Experimentation in real cloud environments for training Deep Reinforcement Learning (DRL) agents can be costly, time-consuming, and non-repeatable. To overcome these limitations, simulation-based approaches are promising alternatives. This paper introduces a specialized simulation environment that integrates OpenAI Gym, a popular platform for reinforcement learning, with CloudSim Plus, a versatile cloud simulation framework. The proposed simulator specifically focuses on the case study of energy-driven cloud scaling. By leveraging the strengths of both Python-based OpenAI Gym and Java-based CloudSim Plus, the simulation environment offers a flexible and extensible platform for DRL-Agent training. The integration is facilitated through a gateway that enables seamless interaction between the two frameworks. The simulation environment is designed to support the training process of DRL agents, enabling them to tackle the complexities of cloud scaling in an energy-aware context. It provides configurable settings that represent various cloud scaling scenarios, allowing researchers to explore different parameter configurations and evaluate the performance of DRL agents effectively. Through extensive experimentation, the proposed simulation environment demonstrates its functionality and applicability in measuring the performance of DRL agents with respect to energy-driven cloud scaling. The results obtained from the case study validate the effectiveness and potential of the simulation environment for training DRL agents in cloud scaling scenarios. Overall, this work presents a novel simulation environment that bridges the gap between DRL-Agent training and cloud scaling challenges, offering researchers a valuable tool for advancing the field of energy-driven cloud scaling through reinforcement learning. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Simulation Modelling Practice & Theory is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.simpat.2023.102858 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Deep reinforcement learning Type: general – SubjectFull: Reinforcement learning Type: general – SubjectFull: Research personnel Type: general – SubjectFull: Ecology Type: general Titles: – TitleFull: Integrating OpenAI Gym and CloudSim Plus: A simulation environment for DRL Agent training in energy-driven cloud scaling. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Agos Jawaddi, Siti Nuraishah – PersonEntity: Name: NameFull: Ismail, Azlan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 1569190X Numbering: – Type: volume Value: 130 Titles: – TitleFull: Simulation Modelling Practice & Theory Type: main |
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