Low-Cost Resource Scheduling Framework using Collaborative Edge Fog Environment for Smart Health Article Swipe
Kiran Deep Singh
,
Prabhdeep Singh
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.22541/au.169164909.95409973/v1
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.22541/au.169164909.95409973/v1
The exponential growth in data processing and resource requirements directly results from the widespread use of smart health applications. Fog computing emerges as a viable paradigm, bringing cloud capabilities to the network’s edge to meet the high computational needs. In this research, we present a low-cost resource scheduling technique for smart health systems that use collaborative edge fog computing to enhance efficiency and maximize the allocation of available resources. The proposed framework uses the network’s edge nodes to distribute computing and storage tasks, which decreases latency, increases scalability, and lowers infrastructure costs. Our resource allocation system dynamically assigns tasks to fog devices and servers based on job priorities, device capabilities, and resource consumption levels. This optimization guarantees consistent workload distribution, resilience in the face of errors, and swift, accurate processing of smart health data. The experimental evaluation verifies the framework’s efficiency in minimizing response times and optimizing resource utilization, a major step forward in smart health. Our Low-Cost Resource Scheduling Framework for Smart Health in a Collaborative Edge Fog Environment enables healthcare providers to provide timely and affordable care. The framework uses edge devices and fog servers to process health-related data closer to data sources and end-users to improve system performance and reduce transmission latency. The framework improves service quality and reduces expenses by decreasing the need for cloud hosting. Edge fog computing’s near-real-time data processing benefits users and patients, strengthening the framework’s smart health application.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.22541/au.169164909.95409973/v1
- https://www.authorea.com/doi/pdf/10.22541/au.169164909.95409973
- OA Status
- gold
- References
- 12
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385721356
All OpenAlex metadata
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https://openalex.org/W4385721356Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.22541/au.169164909.95409973/v1Digital Object Identifier
- Title
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Low-Cost Resource Scheduling Framework using Collaborative Edge Fog Environment for Smart HealthWork title
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-08-10Full publication date if available
- Authors
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Kiran Deep Singh, Prabhdeep SinghList of authors in order
- Landing page
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https://doi.org/10.22541/au.169164909.95409973/v1Publisher landing page
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https://www.authorea.com/doi/pdf/10.22541/au.169164909.95409973Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.authorea.com/doi/pdf/10.22541/au.169164909.95409973Direct OA link when available
- Concepts
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Computer science, Cloud computing, Edge device, Edge computing, Server, Scalability, Scheduling (production processes), Distributed computing, Computer network, Database, Engineering, Operating system, Operations managementTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
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12Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.network’s | 31, 74 |
| abstract_inverted_index.performance | 200 |
| abstract_inverted_index.priorities, | 107 |
| abstract_inverted_index.application. | 235 |
| abstract_inverted_index.capabilities | 28 |
| abstract_inverted_index.experimental | 135 |
| abstract_inverted_index.optimization | 115 |
| abstract_inverted_index.requirements | 8 |
| abstract_inverted_index.scalability, | 87 |
| abstract_inverted_index.transmission | 203 |
| abstract_inverted_index.utilization, | 148 |
| abstract_inverted_index.Collaborative | 166 |
| abstract_inverted_index.applications. | 18 |
| abstract_inverted_index.capabilities, | 109 |
| abstract_inverted_index.collaborative | 55 |
| abstract_inverted_index.computational | 37 |
| abstract_inverted_index.computing’s | 222 |
| abstract_inverted_index.distribution, | 119 |
| abstract_inverted_index.framework’s | 139, 232 |
| abstract_inverted_index.strengthening | 230 |
| abstract_inverted_index.health-related | 189 |
| abstract_inverted_index.infrastructure | 90 |
| abstract_inverted_index.near-real-time | 223 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 2 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.4300000071525574 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.12372387 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |