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
Hlavní autoři: Shruthi, G., Mundada, Monica R., Sowmya, B. J., Supreeth, S.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Hindawi 28.08.2022
John Wiley & Sons, Inc
Wiley
Témata:
Access control
Algorithms
Alternations
Analysis
Bandwidths
Cloud computing
Communication
Cooperation
Data processing
Energy consumption
Energy efficiency
Fault tolerance
Health care industry
Heterogeneity
Internet of Things
Literature reviews
Machine learning
Mathematical optimization
Medical care
Network latency
Neural networks
Optimization
Provisioning
Quality management
Resource allocation
Resource scheduling
Scheduling
Spectrum allocation
Task scheduling
User requirements
Workloads
ISSN:1687-9724, 1687-9732
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Shrnutí: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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ISSN:1687-9724
1687-9732
DOI:10.1155/2022/2131699