Location-aware job scheduling for IoT systems using cloud and fog
The rapid growth of Internet of Things (IoT) applications generates vast volumes of high-speed data streams from numerous devices. Cloud computing solutions often handle and manage this data; however, for certain applications, the latency introduced by transmitting data from edge devices to the clou...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016824010792 |
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| _version_ | 1841553834168549376 |
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| author | Xiaomo Yu Mingjun Zhu Menghan Zhu Xiaomeng Zhou Long Long Mahdi khodaparast |
| author_facet | Xiaomo Yu Mingjun Zhu Menghan Zhu Xiaomeng Zhou Long Long Mahdi khodaparast |
| author_sort | Xiaomo Yu |
| collection | DOAJ |
| description | The rapid growth of Internet of Things (IoT) applications generates vast volumes of high-speed data streams from numerous devices. Cloud computing solutions often handle and manage this data; however, for certain applications, the latency introduced by transmitting data from edge devices to the cloud may be unacceptable. This issue is exacerbated by the bandwidth constraints of public networks, which become significant barriers in large-scale IoT implementations. Consequently, effective resource management, service management, data storage, and power management require more robust infrastructure and complex protocols. An ''intelligent gateway'' based on fog computing can enhance the efficient utilization of cloud and network resources. Resource planning and management in a fog-cloud environment significantly impact system performance, particularly latency. This problem is known to be NP-hard. This paper addresses the challenge of lifetime optimization for scheduling data-intensive tasks in fog and cloud-based IoT systems. Initially, we propose an Integer Linear Programming (ILP) optimization model to frame the problem. Subsequently, we introduce a heuristic method named Data-Locality Aware Job Scheduling in Fog-Cloud (DLSFC), which is derived from the proposed formulation. The effectiveness of DLSFC is evaluated across various system characteristics, with results demonstrating that DLSFC achieves solutions within 85 % of the ideal outcome provided by the LP solver. |
| format | Article |
| id | doaj-art-953c74bff4154673a7d0ed7d70b05bb8 |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-953c74bff4154673a7d0ed7d70b05bb82025-01-09T06:13:16ZengElsevierAlexandria Engineering Journal1110-01682025-01-01110346362Location-aware job scheduling for IoT systems using cloud and fogXiaomo Yu0Mingjun Zhu1Menghan Zhu2Xiaomeng Zhou3Long Long4Mahdi khodaparast5Department of Logistics Management and Engineering, Nanning Normal University, Nanning, Guangxi 530001, China; Guangxi Key Lab of Human-machine Interaction and Intelligent Decision, Nanning Normal University, Nanning, Guangxi 530001, ChinaDepartment of Logistics Management and Engineering, Nanning Normal University, Nanning, Guangxi 530001, ChinaDepartment of Logistics Management and Engineering, Nanning Normal University, Nanning, Guangxi 530001, ChinaDepartment of Logistics Management and Engineering, Nanning Normal University, Nanning, Guangxi 530001, ChinaCollege of Computer Science and Information Engineering, Nanning Normal University, Nanning, Guangxi 530001, China; Guangxi Key Lab of Human-machine Interaction and Intelligent Decision, Nanning Normal University, Nanning, Guangxi 530001, China; Corresponding author at: College of Computer Science and Information Engineering, Nanning Normal University, Nanning, Guangxi 530001, China.Research Assistant Professor, Department of Human Resources Studies and Evaluation, Research Center of Knowledge-based Businesses and Resource Management Studies, IranThe rapid growth of Internet of Things (IoT) applications generates vast volumes of high-speed data streams from numerous devices. Cloud computing solutions often handle and manage this data; however, for certain applications, the latency introduced by transmitting data from edge devices to the cloud may be unacceptable. This issue is exacerbated by the bandwidth constraints of public networks, which become significant barriers in large-scale IoT implementations. Consequently, effective resource management, service management, data storage, and power management require more robust infrastructure and complex protocols. An ''intelligent gateway'' based on fog computing can enhance the efficient utilization of cloud and network resources. Resource planning and management in a fog-cloud environment significantly impact system performance, particularly latency. This problem is known to be NP-hard. This paper addresses the challenge of lifetime optimization for scheduling data-intensive tasks in fog and cloud-based IoT systems. Initially, we propose an Integer Linear Programming (ILP) optimization model to frame the problem. Subsequently, we introduce a heuristic method named Data-Locality Aware Job Scheduling in Fog-Cloud (DLSFC), which is derived from the proposed formulation. The effectiveness of DLSFC is evaluated across various system characteristics, with results demonstrating that DLSFC achieves solutions within 85 % of the ideal outcome provided by the LP solver.http://www.sciencedirect.com/science/article/pii/S1110016824010792SchedulingCloud computingInternet of thingsFog ComputingLocality-awareOptimization |
| spellingShingle | Xiaomo Yu Mingjun Zhu Menghan Zhu Xiaomeng Zhou Long Long Mahdi khodaparast Location-aware job scheduling for IoT systems using cloud and fog Alexandria Engineering Journal Scheduling Cloud computing Internet of things Fog Computing Locality-aware Optimization |
| title | Location-aware job scheduling for IoT systems using cloud and fog |
| title_full | Location-aware job scheduling for IoT systems using cloud and fog |
| title_fullStr | Location-aware job scheduling for IoT systems using cloud and fog |
| title_full_unstemmed | Location-aware job scheduling for IoT systems using cloud and fog |
| title_short | Location-aware job scheduling for IoT systems using cloud and fog |
| title_sort | location aware job scheduling for iot systems using cloud and fog |
| topic | Scheduling Cloud computing Internet of things Fog Computing Locality-aware Optimization |
| url | http://www.sciencedirect.com/science/article/pii/S1110016824010792 |
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