Transparent Peer Review By Scholar9
Intelligent Model Implementation for Resource Sharing in Vehicular Computing
Abstract
Fog computing is among the most significant new concepts in recent technological advancement. It addresses various issues of cloud computing by delivering computing, connectivity, storage, and actual services closer to end devices. Conversely, as systems become more automated, the number of task executions by fog devices grows, necessitating the inclusion of more fog devices. In this paper, we suggest an intelligent Scheduling framework that enhances the usage of current resources instead of installing more fog sources. It has an extra layer called Master Fog (MF) between each cloud, and Vehicular fogs termed Vehicular Fogs resource (VFR). The MF is in a better position to decide on VFR and cloud deployment. The Comparative Attributes Algorithm (CAA) is used to prioritize jobs. A meta-heuristic Grey Wolf Optimizer (GWO) algorithm is used to choose the most accessible VFR with the most excellent computing capabilities. The final findings demonstrate a significant reduction in energy consumption compared to the basic design in order to reach the best network efficiency, as well as essential advantages represented in the increase of bandwidth availability and efficient utilization of other sources.
Shreyas Mahimkar Reviewer
17 Sep 2024 03:54 PM
Approved
Relevance and Originality
The research tackles a critical issue in cloud computing by proposing a new scheduling framework for fog computing, addressing the need for efficient data processing. The introduction of a Master Fog (MF) layer and Vehicular Fog Resources (VFR) is a novel approach. However, the paper would benefit from a more detailed comparison with existing solutions to better emphasize its novelty and impact.
Methodology
The methodology is strong, utilizing the Comparative Attributes Algorithm (CAA) for job prioritization and the Grey Wolf Optimizer (GWO) for selecting the best Vehicular Fog Resources (VFR). Yet, the paper could improve by explaining why these algorithms were chosen over others. Clarifying the selection criteria and comparing with alternative methods would enhance the methodological approach.
Validity & Reliability
The results showing energy reduction and improved network efficiency are promising but need more validation details. The Research Article should provide more information on the experimental setup, including test environments and configurations, to ensure the findings are robust and reliable.
Clarity and Structure
The Research Article is generally well-organized but lacks sufficient detail on the Master Fog (MF) layer and Vehicular Fog Resources (VFR). More detailed explanations and illustrations of these components and their interactions would improve clarity and help readers better understand the proposed framework.
Result Analysis
The results showing reduced energy consumption and increased network efficiency are notable. However, the Research Article would be stronger with quantitative metrics and a comparative analysis with existing solutions. Including case studies or practical applications would further validate the effectiveness and real-world relevance of the framework.
IJ Publication Publisher
Thank You Sir
Shreyas Mahimkar Reviewer