research-article

Personalized Service Delivery using Reinforcement Learning in Fog and Cloud Environment

Authors:

Chinmaya Kumar Dehury,

Satish Narayana SriramaAuthors Info & Claims

iiWAS2019: Proceedings of the 21st International Conference on Information Integration and Web-based Applications & Services

Pages 522 - 529

https://doi.org/10.1145/3366030.3366055

Published: 22 February 2020 Publication History

Abstract

The ability to fulfil the resource demand in runtime is encouraging the businesses to migrate to cloud. Recently, to provide real-time cloud services and to save network resources, fog computing is introduced. To further improve the quality of service in delivery process, Artificial Intelligence is being applied extensively. However, the state-of-the-art in this regard is still immature as it mainly focuses at either fog or cloud. To address this issue, a novel reinforcement learning-based personalized service delivery (RLPSD) mechanism is proposed in this paper, which allows the service provider to combine the fog and cloud environments, while providing the service. RLPSD distributes the user's service requests between fog and cloud, considering the users' constraints (e.g. the distance from fog), thus resulting in personalized service delivery. The proposed RLPSD algorithm is implemented and evaluated in terms of its success rate, percentage of service requests' distribution, learning rate, discount factor, etc.

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Cited By

Mashal HRezvani M(2024)Multiobjective Offloading Optimization in Fog Computing Using Deep Reinforcement LearningJournal of Computer Networks and Communications10.1155/2024/62555112024:1Online publication date: 26-Sep-2024
https://doi.org/10.1155/2024/6255511
Misirli JCasalicchio E(2023)An Analysis of Methods and Metrics for Task Scheduling in Fog ComputingFuture Internet10.3390/fi1601001616:1(16)Online publication date: 30-Dec-2023
https://doi.org/10.3390/fi16010016
Dehury CKumar Donta PDustdar SSrirama S(2022)CCEI-IoT: Clustered and Cohesive Edge Intelligence in Internet of Things2022 IEEE International Conference on Edge Computing and Communications (EDGE)10.1109/EDGE55608.2022.00017(33-40)Online publication date: Jul-2022
https://doi.org/10.1109/EDGE55608.2022.00017

Index Terms

Personalized Service Delivery using Reinforcement Learning in Fog and Cloud Environment

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cover image ACM Other conferences

iiWAS2019: Proceedings of the 21st International Conference on Information Integration and Web-based Applications & Services

December 2019

709 pages

ISBN:9781450371797

DOI:10.1145/3366030

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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JKU: Johannes Kepler Universität Linz
@WAS: International Organization of Information Integration and Web-based Applications and Services

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 February 2020

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H2020 LEIT Information and Communication Technologies

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iiWAS2019

iiWAS2019: The 21st International Conference on Information Integration and Web-based Applications & Services

December 2 - 4, 2019

Munich, Germany

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Cited By

Mashal HRezvani M(2024)Multiobjective Offloading Optimization in Fog Computing Using Deep Reinforcement LearningJournal of Computer Networks and Communications10.1155/2024/62555112024:1Online publication date: 26-Sep-2024
https://doi.org/10.1155/2024/6255511
Misirli JCasalicchio E(2023)An Analysis of Methods and Metrics for Task Scheduling in Fog ComputingFuture Internet10.3390/fi1601001616:1(16)Online publication date: 30-Dec-2023
https://doi.org/10.3390/fi16010016
Dehury CKumar Donta PDustdar SSrirama S(2022)CCEI-IoT: Clustered and Cohesive Edge Intelligence in Internet of Things2022 IEEE International Conference on Edge Computing and Communications (EDGE)10.1109/EDGE55608.2022.00017(33-40)Online publication date: Jul-2022
https://doi.org/10.1109/EDGE55608.2022.00017
Kumar Dehury CNarayana Srirama S(2020)An efficient service dispersal mechanism for fog and cloud computing using deep reinforcement learning2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID)10.1109/CCGrid49817.2020.00-34(589-598)Online publication date: May-2020
https://doi.org/10.1109/CCGrid49817.2020.00-34

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