Flexible Offloading and Task Scheduling for IoT Applications in Dynamic Multi-Access Edge Computing Environments
Nowadays, multi-access edge computing (MEC) has been widely recognized as a promising technology that can support a wide range of new applications for the Internet of Things (IoT). In dynamic MEC networks, the heterogeneous computation capacities of the edge servers and the diversified requirements...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/15/12/2196 |
| _version_ | 1797379270203408384 |
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| author | Yang Sun Yuwei Bian Huixin Li Fangqing Tan Lihan Liu |
| author_facet | Yang Sun Yuwei Bian Huixin Li Fangqing Tan Lihan Liu |
| author_sort | Yang Sun |
| collection | DOAJ |
| description | Nowadays, multi-access edge computing (MEC) has been widely recognized as a promising technology that can support a wide range of new applications for the Internet of Things (IoT). In dynamic MEC networks, the heterogeneous computation capacities of the edge servers and the diversified requirements of the IoT applications are both asymmetric, where and when to offload and schedule the time-dependent tasks of IoT applications remains a challenge. In this paper, we propose a flexible offloading and task scheduling scheme (FLOATS) to adaptively optimize the computation of offloading decisions and scheduling priority sequences for time-dependent tasks in dynamic networks. We model the dynamic optimization problem as a multi-objective combinatorial optimization problem in an infinite time horizon, which is intractable to solve. To address this, a rolling-horizon-based optimization mechanism is designed to decompose the dynamic optimization problem into a series of static sub-problems. A genetic algorithm (GA)-based computation offloading and task scheduling algorithm is proposed for each static sub-problem. This algorithm encodes feasible solutions into two-layer chromosomes, and the optimal solution can be obtained through chromosome selection, crossover and mutation operations. The simulation results demonstrate that the proposed scheme can effectively reduce network costs in comparison to other reference schemes. |
| first_indexed | 2024-03-08T20:19:45Z |
| format | Article |
| id | doaj.art-1f08273ebfc3455d92378e28958340d0 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-08T20:19:45Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-1f08273ebfc3455d92378e28958340d02023-12-22T14:45:22ZengMDPI AGSymmetry2073-89942023-12-011512219610.3390/sym15122196Flexible Offloading and Task Scheduling for IoT Applications in Dynamic Multi-Access Edge Computing EnvironmentsYang Sun0Yuwei Bian1Huixin Li2Fangqing Tan3Lihan Liu4Faculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaFaculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaCICT Mobile Communication Technology Co., Ltd., Beijing 100083, ChinaKey Laboratory of Cognitive Radio and Information Processing, Ministry of Education, Guilin University of Electronic Technology, Guilin 541004, ChinaSchool of Statistics and Data Science, Beijing Wuzi University, Beijing 101149, ChinaNowadays, multi-access edge computing (MEC) has been widely recognized as a promising technology that can support a wide range of new applications for the Internet of Things (IoT). In dynamic MEC networks, the heterogeneous computation capacities of the edge servers and the diversified requirements of the IoT applications are both asymmetric, where and when to offload and schedule the time-dependent tasks of IoT applications remains a challenge. In this paper, we propose a flexible offloading and task scheduling scheme (FLOATS) to adaptively optimize the computation of offloading decisions and scheduling priority sequences for time-dependent tasks in dynamic networks. We model the dynamic optimization problem as a multi-objective combinatorial optimization problem in an infinite time horizon, which is intractable to solve. To address this, a rolling-horizon-based optimization mechanism is designed to decompose the dynamic optimization problem into a series of static sub-problems. A genetic algorithm (GA)-based computation offloading and task scheduling algorithm is proposed for each static sub-problem. This algorithm encodes feasible solutions into two-layer chromosomes, and the optimal solution can be obtained through chromosome selection, crossover and mutation operations. The simulation results demonstrate that the proposed scheme can effectively reduce network costs in comparison to other reference schemes.https://www.mdpi.com/2073-8994/15/12/2196multi-access edge computingcomputation offloadingtask schedulinggenetic algorithm |
| spellingShingle | Yang Sun Yuwei Bian Huixin Li Fangqing Tan Lihan Liu Flexible Offloading and Task Scheduling for IoT Applications in Dynamic Multi-Access Edge Computing Environments Symmetry multi-access edge computing computation offloading task scheduling genetic algorithm |
| title | Flexible Offloading and Task Scheduling for IoT Applications in Dynamic Multi-Access Edge Computing Environments |
| title_full | Flexible Offloading and Task Scheduling for IoT Applications in Dynamic Multi-Access Edge Computing Environments |
| title_fullStr | Flexible Offloading and Task Scheduling for IoT Applications in Dynamic Multi-Access Edge Computing Environments |
| title_full_unstemmed | Flexible Offloading and Task Scheduling for IoT Applications in Dynamic Multi-Access Edge Computing Environments |
| title_short | Flexible Offloading and Task Scheduling for IoT Applications in Dynamic Multi-Access Edge Computing Environments |
| title_sort | flexible offloading and task scheduling for iot applications in dynamic multi access edge computing environments |
| topic | multi-access edge computing computation offloading task scheduling genetic algorithm |
| url | https://www.mdpi.com/2073-8994/15/12/2196 |
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