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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Vydané v:	Cluster computing Ročník 28; číslo 12; s. 796
Hlavní autori:	Momeny, Sajjad, Robatmily, Mohamad, Rahmani, Amir Masoud
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.11.2025 Springer Nature B.V
Predmet:	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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Abstract	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.
AbstractList	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.
ArticleNumber	796
Author	Rahmani, Amir Masoud Robatmily, Mohamad Momeny, Sajjad
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SubjectTerms	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
Title	Multi-objective optimization for the performance of task scheduling in homogeneous fog networks
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