Multi-objective optimization for the performance of task scheduling in homogeneous fog networks

The purpose is to accurately assess fog networks’ overall energy efficiency and spectrum efficiency. This model analyzes the tradeoff between performance improvements and energy consumption in joint task scheduling. The main contribution of this work is the development of an optimized job scheduling...

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Vydáno v:Cluster computing Ročník 28; číslo 12; s. 796
Hlavní autoři: Momeny, Sajjad, Robatmily, Mohamad, Rahmani, Amir Masoud
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.11.2025
Springer Nature B.V
Témata:
Algorithms
Computer Communication Networks
Computer Science
Current modulation
Edge computing
Energy consumption
Energy efficiency
Internet of Things
Mathematical analysis
Multiple objective analysis
Networks
Nodes
Operating Systems
Optimization
Processor Architectures
Quadratic programming
Quality of service
Resource utilization
Task scheduling
Multi-objective optimization
Augmented lagrangian
SQP algorithm
Fog networks
IoT
ISSN:1386-7857, 1573-7543
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Shrnutí:The purpose is to accurately assess fog networks’ overall energy efficiency and spectrum efficiency. This model analyzes the tradeoff between performance improvements and energy consumption in joint task scheduling. The main contribution of this work is the development of an optimized job scheduling algorithm using the Sequential Quadratic Programming (SQP) method to maximize energy and spectrum efficiency in fog networks, significantly outperforming existing task scheduling strategies. It allows us to define the optimization problem of energy efficiency and spectrum efficiency for future smart IoT applications, considering practical limitations in the computational resources of assisting nodes and unused spectrum in nearby environments. Here, a thorough mathematical analysis is used to suggest a job scheduling method that maximizes energy and spectral efficiency. This algorithm aims to determine the most efficient scheduling decision between a node that holds a task and many surrounding nodes that provide assistance. This decision considers the current modulation schemes and time-access methods in use. In addition, our simulations show that the suggested method can achieve much higher energy and spectral efficiency levels than existing task scheduling algorithms across a range of network service settings and situations. The numerical results demonstrated that the SQP algorithm achieved an energy efficiency of 0.0027 bits per joule, outperforming the Augmented Lagrangian method, which achieved 0.0004 bits per joule, while both optimization methods showed higher variability than the traditional scheduling strategy. The proposed SQP algorithm significantly improves energy efficiency compared to current state-of-the-art methods, demonstrating a more advanced and effective solution for optimizing task scheduling in fog networks. This improvement highlights the algorithm’s ability to better balance computational load, energy consumption, and resource utilization, making it a valuable contribution to the field of fog computing and IoT applications.
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ISSN:1386-7857
1573-7543
DOI:10.1007/s10586-025-05526-3