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Hybrid multi objective marine predators algorithm based clustering for lightweight resource scheduling and
1Department of Computer Science and Engineering, K. S. Rangasamy College of Technology, Tiruchengode, Namakkal, 637 215, Tamil Nadu, India. rbaskar@ksrct.ac.in.
Scientific Reports
|May 7, 2025
Summary
This study introduces a new method for efficiently allocating Internet of Things (IoT) applications to fog computing nodes. The Hybrid Multi-Objective Marine Predators Algorithm-based Clustering and Fog Picker (HMMPACFP) technique optimizes resource use and minimizes network latency for better Quality of Service (QoS).
Area of Science:
- Computer Science
- Distributed Systems
- Artificial Intelligence
Background:
- The Internet of Things (IoT) necessitates fog computing for applications requiring low latency and user proximity, complementing traditional cloud infrastructure.
- Efficient allocation and scheduling of IoT applications within fog environments are crucial for realistic deployment and optimal performance.
- Existing scheduling methods face multi-objective challenges, including resource waste, network latency, and maximizing Quality of Service (QoS) on fog nodes.
Purpose of the Study:
- To develop a novel combinatorial technique for fog node allocation and dynamic scheduling of IoT applications.
- To address the multi-objective nature of fog scheduling by optimizing resource utilization, minimizing network latency, and enhancing QoS.
- To introduce a lightweight technique for efficient IoT application deployment in fog environments.
Main Methods:
- Development of the Hybrid Multi-Objective Marine Predators Algorithm-based Clustering and Fog Picker (HMMPACFP) technique.
- Utilizing the Fog Picker component for allocating IoT components to fog nodes based on predefined QoS parameters.
- Conducting simulation trials using iMetal and iFogSim, evaluating performance with Hypervolume (HV) and Generational Distance (IGD) metrics.
Main Results:
- The proposed HMMPACFP scheme demonstrated superior performance compared to benchmarked methodologies.
- The integration of Fog Picker with HMMPACFP achieved 32.18% faster convergence and 26.92% greater solution variety.
- The combined approach showed an improved balance between exploration and exploitation rates in the optimization process.
Conclusions:
- The HMMPACFP technique offers an effective solution for dynamic scheduling and resource allocation in fog computing environments.
- The developed method successfully addresses the multi-objective challenges inherent in fog node allocation for IoT applications.
- The findings highlight the potential of HMMPACFP for enhancing the efficiency and performance of IoT deployments in fog computing.
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