Resource Scheduling for UAV-Assisted Failure-Prone MEC in Industrial Internet
This paper focuses on reducing execution delays of dynamic computing tasks in UAV-assisted fault-prone mobile edge computing (FP-MEC) systems, which combine mobile edge computing (MEC) and network function virtualization (NFV) technologies. FP-MEC is suited to meet Industrial Internet (IIN) requirem...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-04-01
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| Series: | Drones |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-446X/7/4/259 |
| _version_ | 1827745293556252672 |
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| author | Xuehua Li Yu Fang Chunyu Pan Yuanxin Cai Mingyu Zhou |
| author_facet | Xuehua Li Yu Fang Chunyu Pan Yuanxin Cai Mingyu Zhou |
| author_sort | Xuehua Li |
| collection | DOAJ |
| description | This paper focuses on reducing execution delays of dynamic computing tasks in UAV-assisted fault-prone mobile edge computing (FP-MEC) systems, which combine mobile edge computing (MEC) and network function virtualization (NFV) technologies. FP-MEC is suited to meet Industrial Internet (IIN) requirements such as data privacy, low latency, and low-cost industrial scalability in specific scenarios. However, the reliability of virtual network functions (VNFs) deployed on UAVs could impact system performance. Thus, this paper proposes the dynamic task scheduling optimization algorithm (DTSOA) based on deep reinforcement learning (DRL) for resource allocation design. The formulated execution delay optimization problem is described as an integer linear programming problem and it is an NP-hard problem. To overcome the intractable problem, this paper discretizes it into a series of single-time slot optimization problems. Furthermore, the experimental rigor is improved by constructing a real-time server state update system to calculate the real-time server load situation and crash probability. Theoretical analysis and experiments show that the DTSOA has better application prospects than Q-learning and the recent search method (RSM), and it is closer to the traversal search method (TSM). |
| first_indexed | 2024-03-11T05:05:02Z |
| format | Article |
| id | doaj.art-6370cf9cdcf64bb7b4009276f89aaefe |
| institution | Directory Open Access Journal |
| issn | 2504-446X |
| language | English |
| last_indexed | 2024-03-11T05:05:02Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj.art-6370cf9cdcf64bb7b4009276f89aaefe2023-11-17T18:58:13ZengMDPI AGDrones2504-446X2023-04-017425910.3390/drones7040259Resource Scheduling for UAV-Assisted Failure-Prone MEC in Industrial InternetXuehua Li0Yu Fang1Chunyu Pan2Yuanxin Cai3Mingyu Zhou4Key Laboratory of Modern Measurement and Control Technology, Ministry of Education, Beijing Information Science and Technology University, Beijing 100101, ChinaKey Laboratory of Modern Measurement and Control Technology, Ministry of Education, Beijing Information Science and Technology University, Beijing 100101, ChinaKey Laboratory of Modern Measurement and Control Technology, Ministry of Education, Beijing Information Science and Technology University, Beijing 100101, ChinaKey Laboratory of Modern Measurement and Control Technology, Ministry of Education, Beijing Information Science and Technology University, Beijing 100101, ChinaBaicells Technologies Co., Ltd., Beijing 100094, ChinaThis paper focuses on reducing execution delays of dynamic computing tasks in UAV-assisted fault-prone mobile edge computing (FP-MEC) systems, which combine mobile edge computing (MEC) and network function virtualization (NFV) technologies. FP-MEC is suited to meet Industrial Internet (IIN) requirements such as data privacy, low latency, and low-cost industrial scalability in specific scenarios. However, the reliability of virtual network functions (VNFs) deployed on UAVs could impact system performance. Thus, this paper proposes the dynamic task scheduling optimization algorithm (DTSOA) based on deep reinforcement learning (DRL) for resource allocation design. The formulated execution delay optimization problem is described as an integer linear programming problem and it is an NP-hard problem. To overcome the intractable problem, this paper discretizes it into a series of single-time slot optimization problems. Furthermore, the experimental rigor is improved by constructing a real-time server state update system to calculate the real-time server load situation and crash probability. Theoretical analysis and experiments show that the DTSOA has better application prospects than Q-learning and the recent search method (RSM), and it is closer to the traversal search method (TSM).https://www.mdpi.com/2504-446X/7/4/259mobile edge computingIndustrial Internetresource allocationvirtual network functionmulti-UAVsdeep reinforcement learning |
| spellingShingle | Xuehua Li Yu Fang Chunyu Pan Yuanxin Cai Mingyu Zhou Resource Scheduling for UAV-Assisted Failure-Prone MEC in Industrial Internet Drones mobile edge computing Industrial Internet resource allocation virtual network function multi-UAVs deep reinforcement learning |
| title | Resource Scheduling for UAV-Assisted Failure-Prone MEC in Industrial Internet |
| title_full | Resource Scheduling for UAV-Assisted Failure-Prone MEC in Industrial Internet |
| title_fullStr | Resource Scheduling for UAV-Assisted Failure-Prone MEC in Industrial Internet |
| title_full_unstemmed | Resource Scheduling for UAV-Assisted Failure-Prone MEC in Industrial Internet |
| title_short | Resource Scheduling for UAV-Assisted Failure-Prone MEC in Industrial Internet |
| title_sort | resource scheduling for uav assisted failure prone mec in industrial internet |
| topic | mobile edge computing Industrial Internet resource allocation virtual network function multi-UAVs deep reinforcement learning |
| url | https://www.mdpi.com/2504-446X/7/4/259 |
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