Enhanced novelty approaches for resource allocation model for multi-cloud environment in vehicular Ad-Hoc networks

As the number of service requests for applications continues increasing due to various conditions, the limitations on the number of resources provide a barrier in providing the applications with the appropriate Quality of Service (QoS) assurances. As a result, an efficient scheduling mechanism is re...

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Veröffentlicht in:Scientific reports Jg. 15; H. 1; S. 9472 - 18
Hauptverfasser: Isaac, R. Augustian, Sundaravadivel, P., Marx, V. S. Nici, Priyanga, G.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 19.03.2025
Nature Publishing Group
Nature Portfolio
Schlagworte:
639/705
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639/705/258
Algorithms
Cloud server
Crossover and mutation (CM)
Elephant herding Lion optimizer (EHLO)
Herding
Humanities and Social Sciences
Marine predator algorithm (MPA)
Modified fuzzy c-means (MFCM) algorithm
multidisciplinary
Resource allocation
Science
Science (multidisciplinary)
Storage capacity
Vehicular edge computing (VEC)
Vehicle
Elephant herding Lion optimizer (EHLO)
Cloud server
Marine predator algorithm (MPA)
Modified fuzzy c-means (MFCM) algorithm
Vehicular edge computing (VEC)
Crossover and mutation (CM)
ISSN:2045-2322, 2045-2322
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Zusammenfassung:As the number of service requests for applications continues increasing due to various conditions, the limitations on the number of resources provide a barrier in providing the applications with the appropriate Quality of Service (QoS) assurances. As a result, an efficient scheduling mechanism is required to determine the order of handling application requests, as well as the appropriate use of a broadcast media and data transfer. In this paper an innovative approach, incorporating the Crossover and Mutation (CM)-centered Marine Predator Algorithm (MPA) is introduced for an effective resource allocation. This strategic resource allocation optimally schedules resources within the Vehicular Edge computing (VEC) network, ensuring the most efficient utilization. The proposed method begins by the meticulous feature extraction from the Vehicular network model, with attributes such as mobility patterns, transmission medium, bandwidth, storage capacity, and packet delivery ratio. For further analysis the Elephant Herding Lion Optimizer (EHLO) algorithm is employed to pinpoint the most critical attributes. Subsequently the Modified Fuzzy C-Means (MFCM) algorithm is used for efficient vehicle clustering centred on selected attributes. These clustered vehicle characteristics are then transferred and stored within the cloud server infrastructure. The performance of the proposed methodology is evaluated using MATLAB software using simulation method. This study offers a comprehensive solution to the resource allocation challenge in Vehicular Cloud Networks, addresses the burgeoning demands of modern applications while ensuring QoS assurances and signifies a significant advancement in the field of VEC.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-93365-y