Mayfly Taylor Optimisation-Based Scheduling Algorithm with Deep Reinforcement Learning for Dynamic Scheduling in Fog-Cloud Computing
Fog computing domain plays a prominent role in supporting time-delicate applications, which are associated with smart Internet of Things (IoT) services, like smart healthcare and smart city. However, cloud computing is a capable standard for IoT in data processing owing to the high latency restricti...
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| Vydáno v: | Applied Computational Intelligence and Soft Computing Ročník 2022; s. 1 - 17 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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New York
Hindawi
28.08.2022
John Wiley & Sons, Inc Wiley |
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| ISSN: | 1687-9724, 1687-9732 |
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| Abstract | Fog computing domain plays a prominent role in supporting time-delicate applications, which are associated with smart Internet of Things (IoT) services, like smart healthcare and smart city. However, cloud computing is a capable standard for IoT in data processing owing to the high latency restriction of the cloud, and it is incapable of satisfying needs for time-sensitive applications. The resource provisioning and allocation process in fog-cloud structure considers dynamic alternations in user necessities, and also restricted access resources in fog devices are more challenging. The global adoption of IoT-driven applications has led to the rise of fog computing structure, which permits perfect connection for mobile edge and cloud resources. The effectual scheduling of application tasks in fog environments is a challenging task because of resource heterogeneity, stochastic behaviours, network hierarchy, controlled resource abilities, and mobility elements in IoT. The deadline is the most significant challenge in the fog computing structure due to the dynamic variations in user requirement parameters. In this paper, Mayfly Taylor Optimisation Algorithm (MTOA) is developed for dynamic scheduling in the fog-cloud computing model. The developed MTOA-based Deep Q-Network (DQN) showed better performance with energy consumption, service level agreement (SLA), and computation cost of 0.0162, 0.0114, and 0.0855, respectively. |
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| AbstractList | Fog computing domain plays a prominent role in supporting time-delicate applications, which are associated with smart Internet of Things (IoT) services, like smart healthcare and smart city. However, cloud computing is a capable standard for IoT in data processing owing to the high latency restriction of the cloud, and it is incapable of satisfying needs for time-sensitive applications. The resource provisioning and allocation process in fog-cloud structure considers dynamic alternations in user necessities, and also restricted access resources in fog devices are more challenging. The global adoption of IoT-driven applications has led to the rise of fog computing structure, which permits perfect connection for mobile edge and cloud resources. The effectual scheduling of application tasks in fog environments is a challenging task because of resource heterogeneity, stochastic behaviours, network hierarchy, controlled resource abilities, and mobility elements in IoT. The deadline is the most significant challenge in the fog computing structure due to the dynamic variations in user requirement parameters. In this paper, Mayfly Taylor Optimisation Algorithm (MTOA) is developed for dynamic scheduling in the fog-cloud computing model. The developed MTOA-based Deep Q-Network (DQN) showed better performance with energy consumption, service level agreement (SLA), and computation cost of 0.0162, 0.0114, and 0.0855, respectively. |
| Audience | Academic |
| Author | Mundada, Monica R. Supreeth, S. Shruthi, G. Sowmya, B. J. |
| Author_xml | – sequence: 1 givenname: G. orcidid: 0000-0002-8185-8134 surname: Shruthi fullname: Shruthi, G. organization: M S Ramaiah Institute of TechnologyVTUBengaluru 560054Indiamsrit.edu – sequence: 2 givenname: Monica R. orcidid: 0000-0001-7262-8997 surname: Mundada fullname: Mundada, Monica R. organization: Department of Computer Science and EngineeringM S Ramaiah Institute of TechnologyBengaluru 560054Indiamsrit.edu – sequence: 3 givenname: B. J. surname: Sowmya fullname: Sowmya, B. J. organization: Department of Computer Science and EngineeringM S Ramaiah Institute of TechnologyBengaluru 560054Indiamsrit.edu – sequence: 4 givenname: S. orcidid: 0000-0002-7097-6733 surname: Supreeth fullname: Supreeth, S. organization: School of CSEREVA UniversityBengaluru 560064Indiareva.edu.in |
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| Copyright | Copyright © 2022 G. Shruthi et al. COPYRIGHT 2022 John Wiley & Sons, Inc. Copyright © 2022 G. Shruthi et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 |
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| Title | Mayfly Taylor Optimisation-Based Scheduling Algorithm with Deep Reinforcement Learning for Dynamic Scheduling in Fog-Cloud Computing |
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