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Frontiers of Mechanical Engineering

Front. Mech. Eng.    2019, Vol. 14 Issue (3) : 320-331     https://doi.org/10.1007/s11465-019-0542-1
RESEARCH ARTICLE
Real-time task processing method based on edge computing for spinning CPS
Shiyong YIN, Jinsong BAO(), Jie LI, Jie ZHANG
College of Mechanical Engineering, Donghua University, Shanghai 201620, China
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Abstract

Spinning production is a typical continuous manufacturing process characterized by high speed and uncertain dynamics. Each manufacturing unit in spinning production produces various real-time tasks, which may affect production efficiency and yarn quality if not processed in time. This paper presents an edge computing-based method that is different from traditional centralized cloud computation because its decentralization characteristics meet the high-speed and high-response requirements of yarn production. Edge computing nodes, real-time tasks, and edge computing resources are defined. A system model is established, and a real-time task processing method is proposed for the edge computing scenario. Experimental results indicate that the proposed real-time task processing method based on edge computing can effectively solve the delay problem of real-time task processing in spinning cyber-physical systems, save bandwidth, and enhance the security of task transmission.

Keywords edge computing      real-time task      scheduling      CPS      spinning     
Corresponding Authors: Jinsong BAO   
Just Accepted Date: 31 May 2019   Online First Date: 03 July 2019    Issue Date: 24 July 2019
 Cite this article:   
Shiyong YIN,Jinsong BAO,Jie LI, et al. Real-time task processing method based on edge computing for spinning CPS[J]. Front. Mech. Eng., 2019, 14(3): 320-331.
 URL:  
http://journal.hep.com.cn/fme/EN/10.1007/s11465-019-0542-1
http://journal.hep.com.cn/fme/EN/Y2019/V14/I3/320
Fig.1  Diagram of spinning CPS
No. Task category Example
1 Tasks related to personnel Manually issue control signals, start and stop the machine, etc.
2 Tasks related to equipment Equipment failure, equipment preventive maintenance tips, etc.
3 Tasks related to the item or work in progress Added raw materials, products with anomalies, etc.
4 Tasks related to the process Parameter adjustment, process switching, etc.
5 Tasks related to the environment Adjustment of the control temperature, humidity, etc.
Tab.1  Common types of tasks and examples in spinning CPS
Fig.2  Architecture of spinning CPS in the centralized computing mode
Fig.3  Architecture of spinning CPS edge computing mode. (a) A unique ECN per entity; (b) ECN shared by multiple entities
Fig.4  Components and process logic of ECN. Mfg.: Manufacturing; O: Output; I: Input; t: Task
Fig.5  Framework for describing a task
Fig.6  Experimental diagram
No. Task name Length/s Interface time/ms Scheduler time/s Resource assignment
time/ms
Resource allocation Run time/s Turnaround time/s
1 t1 240 5.7521 0 28.4385 ECN2.1 240.0526 240.0868
2 t2 901 5.9254 ? ? Cloud center ? ?
3 t3 285 5.7945 0 28.5256 ECN2.2 285.0512 285.0855
4 t4 320 5.7789 0 37.9196 ECN2.3 320.0651 320.1088
5 t5 240 5.6214 0 28.6335 ECN2.spare1 240.0489 240.0831
6 t6 350 5.8012 0 27.6193 ECN2.spare2 350.0693 350.1027
7 t7 210 5.6287 0 162.8069 ECN3.spare1 210.0446 210.2130
8 t8 180 5.6146 0 147.578 ECN4.spare1 180.0479 180.2011
9 t9 270 5.6812 0 150.4799 ECN1.spare1 270.0512 270.2073
10 t10 195 5.6349 0 147.3993 ECN1.spare2 195.0456 195.1987
11 t11 305 5.7871 0 162.5112 ECN4.spare1 305.0660 305.2343
12 t12 95 5.4243 0 153.7191 ECN1.spare2 95.0384 95.1975
13 t13 310 5.6479 82.3154 156.2954 ECN1.spare2 310.0732 392.5505
14 t14 25 5.2273 111.0159 24.9676 ECN2.1 25.0080 136.0469
15 t15 72 5.3394 108.3160 140.6214 ECN1.spare2 72.0025 180.4645
Tab.2  Resource allocation and execution of tasks
Fig.7  Resource assignment time of tasks
Fig.8  Resource allocation results
Fig.9  Comparison of delay rates
Fig.10  Effect of number of tasks on the delay rate
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