Dynamic Scheduling Method for Video Intelligent Analysis Tasks Based on Edge Computing Power Collaborative System

LI Chenghua; SHI Shengtao; LI Xiaotian; JIANG Xiaoping; SHI Hongling

doi:10.11999/JEIT221570

Volume 45 Issue 12

Dec. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(12): 4458-4468

LI Chenghua, SHI Shengtao, LI Xiaotian, JIANG Xiaoping, SHI Hongling. Dynamic Scheduling Method for Video Intelligent Analysis Tasks Based on Edge Computing Power Collaborative System[J]. Journal of Electronics & Information Technology, 2023, 45(12): 4458-4468. doi: 10.11999/JEIT221570

Citation:

LI Chenghua, SHI Shengtao, LI Xiaotian, JIANG Xiaoping, SHI Hongling. Dynamic Scheduling Method for Video Intelligent Analysis Tasks Based on Edge Computing Power Collaborative System[J]. Journal of Electronics & Information Technology, 2023, 45(12): 4458-4468. doi: 10.11999/JEIT221570

Citation:

LI Chenghua, SHI Shengtao, LI Xiaotian, JIANG Xiaoping, SHI Hongling. Dynamic Scheduling Method for Video Intelligent Analysis Tasks Based on Edge Computing Power Collaborative System[J]. Journal of Electronics & Information Technology, 2023, 45(12): 4458-4468. doi: 10.11999/JEIT221570

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Dynamic Scheduling Method for Video Intelligent Analysis Tasks Based on Edge Computing Power Collaborative System

doi: 10.11999/JEIT221570

LI Chenghua^{1, 2},
SHI Shengtao¹,
LI Xiaotian¹,
JIANG Xiaoping^{1, 2
,
,},
SHI Hongling^{1, 2}

1.
College of Electronic and Information Engineering, South-central University for Nationalities, Wuhan, 430074, China
2.
Hubei Province Key Laboratory of Intelligent Wireless Communication, Wuhan, 430074, China

Funds: The National Key R&D Program of China (2020YFC1522600), The South-Central University for Nationalities (CZT20001)

Received Date: 2022-12-23
Accepted Date: 2023-07-31
Rev Recd Date: 2023-07-24

Available Online: 2023-08-07

Publish Date: 2023-12-26

Abstract

Abstract

Intelligent analysis of surveillance video data based on deep learning models can improve the cultural relics security risk prevention capabilities of cultural relics museum units. In view of the needs of cultural relics museum units to make full use of existing free and available computing resources to complete more intelligent analysis of video data, a dynamic scheduling method for video intelligent analysis tasks is proposed. The device serves as a computing node to form an edge computing power collaborative system to process intelligent video analysis tasks. In this paper, the problem to be solved is modeled as a two-dimensional multiple knapsack problem, and the method of dynamic programming is used to solve how to allocate dynamically video analysis tasks on the edge computing power collaborative system so that the security value and benefits obtained by the system execution tasks in each time period problem of maximization. The simulation results show that the proposed method can dynamically allocate video intelligent analysis tasks based on the monitoring and analysis of the current resource usage status of the system without interfering with the normal business application services of cultural relics museum units, achieving the goal of maximizing security value and benefits purpose.
- Task scheduling,
- Source constraints,
- Intelligent video analysis,
- Cultural relics security

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References(17)

References

[1]	DENG Ruilong, LU Rongxing, LAI Chengzhe, et al. Optimal workload allocation in fog-cloud computing toward balanced delay and power consumption[J]. IEEE Internet of Things Journal, 2016, 3(6): 1171–1181. doi: 10.1109/JIOT.2016.2565516
[2]	FAN Qiang and ANSARI N. Towards workload balancing in fog computing empowered IoT[J]. IEEE Transactions on Network Science and Engineering, 2020, 7(1): 253–262. doi: 10.1109/TNSE.2018.2852762
[3]	YOUSEFPOUR A, ISHIGAKI G, GOUR R, et al. On reducing IoT service delay via fog offloading[J]. IEEE Internet of Things Journal, 2018, 5(2): 998–1010. doi: 10.1109/JIOT.2017.2788802
[4]	KIM W S and CHUNG S H. User-participatory fog computing architecture and its management schemes for improving feasibility[J]. IEEE Access, 2018, 6: 20262–20278. doi: 10.1109/ACCESS.2018.2815629
[5]	AL-KHAFAJIY M, BAKER T, AL-LIBAWY H, et al. Fog computing framework for internet of things applications[C]. The 11th International Conference on Developments in eSystems Engineering, Cambridge, UK, 2018: 71–77.
[6]	BONOMI F, MILITO R, ZHU Jiang, et al. Fog computing and its role in the internet of things[C]. The First Edition of the MCC Workshop on Mobile Cloud Computing, Helsinki, Finland, 2012: 13–16.
[7]	许方敏, 伍丽娇, 王翔, 等. 5G上行链路中基于预测的紧急资源分配方法研究[J]. 电子与信息学报, 2022, 44(2): 611–619. doi: 10.11999/JEIT201050 XU Fangmin, WU Lijiao, WANG Xiang, et al. Research on prediction based emergency resource allocation in 5G uplink[J]. Journal of Electronics &Information Technology, 2022, 44(2): 611–619. doi: 10.11999/JEIT201050
[8]	KATTEPUR A, DOHARE H, MUSHUNURI V, et al. Resource constrained offloading in fog computing[C]. The 1st Workshop on Middleware for Edge Clouds & Cloudlets, Trento, Italy, 2016: 1–6.
[9]	MENNES R, SPINNEWYN B, LATRÉ S, et al. GRECO: A distributed genetic algorithm for reliable application placement in hybrid clouds[C]. The 5th IEEE International Conference on Cloud Networking, Pisa, Italy, 2016: 14–20.
[10]	SKARLAT O, NARDELLI M, SCHULTE S, et al. Towards QoS-aware fog service placement[C]. The 1st International Conference on Fog and Edge Computing, Madrid, Spain, 2017: 89–96.
[11]	BROGI A and FORTI S. QoS-aware deployment of IoT applications through the fog[J]. IEEE Internet of Things Journal, 2017, 4(5): 1185–1192. doi: 10.1109/JIOT.2017.2701408
[12]	卢为党, 詹悦者, 花俏枝, 等. 基于无人机无线能量传输的边缘计算系统能耗优化方法研究[J]. 电子与信息学报, 2022, 44(3): 899–905. doi: 10.11999/JEIT211314 LU Weidang, ZHAN Yuezhe, HUA Qiaozhi, et al. Energy consumption optimization in UAV wireless power transfer based mobile edge computing system[J]. Journal of Electronics &Information Technology, 2022, 44(3): 899–905. doi: 10.11999/JEIT211314
[13]	MENG Jiaying, TAN Haisheng, LI Xiangyang, et al. Online deadline-aware task dispatching and scheduling in edge computing[J]. IEEE Transactions on Parallel and Distributed Systems, 2020, 31(6): 1270–1286. doi: 10.1109/TPDS.2019.2961905
[14]	LIU Shengyu, QI Xiaogang, and LIU Lifang. Multi-objective task scheduling of circuit repair[J]. Axioms, 2022, 11(12): 714. doi: 10.3390/axioms11120714
[15]	张翀宇, 陈彦明, 李炜. 边缘计算中面向数据流的实时任务调度算法[J]. 计算机科学, 2022, 49(7): 263–270. doi: 10.11896/jsjkx.210300195 ZHANG Chongyu, CHEN Yanming, and LI Wei. Task offloading online algorithm for data stream edge computing[J]. Computer Science, 2022, 49(7): 263–270. doi: 10.11896/jsjkx.210300195
[16]	CHEBIL K and KHEMAKHEM M. A dynamic programming algorithm for the knapsack problem with setup[J]. Computers & Operations Research, 2015, 64: 40–50. doi: 10.1016/j.cor.2015.05.005
[17]	HE Yichao, WANG Xizhao, and HE Yulin, et al. Exact and approximate algorithms for discounted {0-1} knapsack problem[J]. Information Sciences, 2016, 369: 634–647. doi: 10.1016/j.ins.2016.07.037