A Comparative Analysis of Dynamic Scheduling Algorithms for Enhanced Resource Management in Homogeneous and Heterogeneous Fog Computing Environments
Fog computing, an emerging paradigm, extends cloud capabilities to the network's edge, enhancing real-time data processing and analysis. This study focuses on optimizing fog computing systems using an adaptive scheduling algorithm. It efficiently categorizes incoming tasks based on their securi...
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| Vydané v: | Procedia computer science Ročník 230; s. 542 - 553 |
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| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier B.V
2023
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| ISSN: | 1877-0509, 1877-0509 |
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| Abstract | Fog computing, an emerging paradigm, extends cloud capabilities to the network's edge, enhancing real-time data processing and analysis. This study focuses on optimizing fog computing systems using an adaptive scheduling algorithm. It efficiently categorizes incoming tasks based on their security requirements into private, semi-private, and public groups, distributing them among Mobile Data Centers (MDCs) and Cloud Data Centers (CDCs). By considering various parameters like job quantities, MDCs, CDCs, their capacities, and communication delays, the algorithm ensures tasks are allocated optimally. The performance of three scheduling algorithms (FCFS, SJF, EDF) is analyzed in both homogeneous and heterogeneous fog environments, shedding light on system efficiency, workload distribution, and resource management. This study provides valuable guidance for the design and operation of efficient fog computing infrastructures tailored to the diverse requirements of IoT applications while optimizing resource utilization. |
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| AbstractList | Fog computing, an emerging paradigm, extends cloud capabilities to the network's edge, enhancing real-time data processing and analysis. This study focuses on optimizing fog computing systems using an adaptive scheduling algorithm. It efficiently categorizes incoming tasks based on their security requirements into private, semi-private, and public groups, distributing them among Mobile Data Centers (MDCs) and Cloud Data Centers (CDCs). By considering various parameters like job quantities, MDCs, CDCs, their capacities, and communication delays, the algorithm ensures tasks are allocated optimally. The performance of three scheduling algorithms (FCFS, SJF, EDF) is analyzed in both homogeneous and heterogeneous fog environments, shedding light on system efficiency, workload distribution, and resource management. This study provides valuable guidance for the design and operation of efficient fog computing infrastructures tailored to the diverse requirements of IoT applications while optimizing resource utilization. |
| Author | Sehgal, Nikita Bansal, Savina Bansal, RK |
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| Cites_doi | 10.1109/TPDS.2003.1206502 10.3390/electronics12061452 10.3233/MGS-160252 10.1016/j.sysarc.2020.101743 10.46647/ijetms.2023.v07i01.009 10.1016/j.jnca.2019.06.006 10.1016/j.future.2019.07.010 10.1016/j.future.2008.12.001 10.1002/cpe.4124 10.1007/s11042-018-7051-9 10.1016/j.jnca.2022.103333 |
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| Keywords | scheduling Internet of things fog computing resource utilization;optimization;security,energy efficiency |
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volume: 6 start-page: 7912 issue: 5 year: 2019 ident: 10.1016/j.procs.2023.12.110_bib0026 article-title: Fog Computing: Vision, Architecture, and Challenges publication-title: IEEE Internet of Things Journal |
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| SubjectTerms | fog computing Internet of things resource utilization;optimization;security,energy efficiency scheduling |
| Title | A Comparative Analysis of Dynamic Scheduling Algorithms for Enhanced Resource Management in Homogeneous and Heterogeneous Fog Computing Environments |
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