Multi-parametric and priority driven particle swarm (MPPPSO) optimized task scheduling approach for improving performance of fog computing system

Fog computing architecture provides real-time support for service distribution in a smart and IoT-based network. Various industries, hospitals, hostels, and smart environments use the same architecture over cloud computing to optimize the performance and reliability of service distribution. Fog comp...

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Vydáno v:	Progress in artificial intelligence Ročník 14; číslo 3; s. 301 - 318
Hlavní autoři:	Monika, Sehrawat, Harkesh
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2025 Springer Nature B.V
Témata:	Algorithms Architecture Artificial Intelligence Cloud computing Computational Intelligence Computer architecture Computer Imaging Computer Science Control Data Mining and Knowledge Discovery Deadlines Edge computing Failure Failure rates Geographical distribution Hostels Mechatronics Methods Natural Language Processing (NLP) Parameter sensitivity Particle swarm optimization Pattern Recognition and Graphics Real time Regular Paper Reliability Resource allocation Robotics Scheduling Smart houses Task scheduling Vision Fog computing Scheduling Particle swarm optimization Resource allocation
ISSN:	2192-6352, 2192-6360
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Abstract	Fog computing architecture provides real-time support for service distribution in a smart and IoT-based network. Various industries, hospitals, hostels, and smart environments use the same architecture over cloud computing to optimize the performance and reliability of service distribution. Fog computing ensures the handling of geographic distribution, heterogeneous systems, and high-performing computing to achieve the same. But as the load increases over the network, the architecture faces various issues including high processing time, wait time, and task failure in the real environment. In this paper, a load-sensitive and multi-parameter adaptive priority driven swarm model is presented to optimize the performance and reliability of fog computing. The proposed algorithm is implied within the middle layer to optimize the resource allocation at the earlier stage. A dynamic and featured evaluation of fog devices and a parametric mapping of generated tasks are performed to reduce the failure rate and delay in the fog computing environment. Task priority, load, and deadline are the key parameters considered to optimize resource allocation and task scheduling in the real environment. The proposed model is compared against conventional and recent task scheduling methods. These methods include FCFS, SJF, Greedy, Priority-based, Jamil et al., Max–Min, and Aladwani et al. approaches. The experiments are conducted in multiple scenarios with different load and fog devices. The proposed MPPPSO model claimed a significant reduction of 13.38% against FCFS, 15.25% against SJF, 12.88% against Greedy, 9.47% Priority based methods, 8.95% against Jamil et al., 12.81% against Max–Min and 2.31% against Aladwani et al. methods. The results demonstrate that the PSO displays the least effective performance, with a maximum failure rate of 13.18%. The documented failure rates are 12.57% for ACO, 9.46% for ABC, 8.63% for GWO, and 5.67% for WOA algorithms. The proposed MPPPSO algorithm exhibits superior reliability, with a failure rate of 4.23%. The overall results identified a significant improvement in performance and reliability against state-of-art methods.
AbstractList	Fog computing architecture provides real-time support for service distribution in a smart and IoT-based network. Various industries, hospitals, hostels, and smart environments use the same architecture over cloud computing to optimize the performance and reliability of service distribution. Fog computing ensures the handling of geographic distribution, heterogeneous systems, and high-performing computing to achieve the same. But as the load increases over the network, the architecture faces various issues including high processing time, wait time, and task failure in the real environment. In this paper, a load-sensitive and multi-parameter adaptive priority driven swarm model is presented to optimize the performance and reliability of fog computing. The proposed algorithm is implied within the middle layer to optimize the resource allocation at the earlier stage. A dynamic and featured evaluation of fog devices and a parametric mapping of generated tasks are performed to reduce the failure rate and delay in the fog computing environment. Task priority, load, and deadline are the key parameters considered to optimize resource allocation and task scheduling in the real environment. The proposed model is compared against conventional and recent task scheduling methods. These methods include FCFS, SJF, Greedy, Priority-based, Jamil et al., Max–Min, and Aladwani et al. approaches. The experiments are conducted in multiple scenarios with different load and fog devices. The proposed MPPPSO model claimed a significant reduction of 13.38% against FCFS, 15.25% against SJF, 12.88% against Greedy, 9.47% Priority based methods, 8.95% against Jamil et al., 12.81% against Max–Min and 2.31% against Aladwani et al. methods. The results demonstrate that the PSO displays the least effective performance, with a maximum failure rate of 13.18%. The documented failure rates are 12.57% for ACO, 9.46% for ABC, 8.63% for GWO, and 5.67% for WOA algorithms. The proposed MPPPSO algorithm exhibits superior reliability, with a failure rate of 4.23%. The overall results identified a significant improvement in performance and reliability against state-of-art methods.
Author	Sehrawat, Harkesh Monika
Author_xml	– sequence: 1 surname: Monika fullname: Monika email: monikacse1920@gmail.com organization: Department of Computer Science & Engineering, UIET, Maharshi Dayanand University – sequence: 2 givenname: Harkesh surname: Sehrawat fullname: Sehrawat, Harkesh organization: Department of Computer Science & Engineering, UIET, Maharshi Dayanand University
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Title	Multi-parametric and priority driven particle swarm (MPPPSO) optimized task scheduling approach for improving performance of fog computing system
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