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Frontiers of Mechanical Engineering

Front. Mech. Eng.    2019, Vol. 14 Issue (3) : 320-331
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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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 Author(s): 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.
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
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
1 K D Kang, C Basaran. Adaptive data replication for load sharing in a sensor data center. In: Proceedings of the 29th IEEE International Conference on Distributed Computing Systems Workshops. Montreal: IEEE, 2009, 20–25
2 H Ahmadi, T Abdelzaher, I Gupta. Congestion control for spatio-temporal data in cyber-physical systems. In: Proceedings of the 1st ACM/IEEE International Conference on Cyber-physical Systems. Stockholm: ACM, 2010, 89–98
3 C J Xue, G L Xing, H Yuan, et al.Joint sleep scheduling and mode assignment in wireless cyber-physical systems. In: Proceedings of the 29th IEEE International Conference on Distributed Computing Systems Workshops. Montreal: IEEE, 2009, 1–6
4 Y F Guo, F X Kong, D K Zhu, et al.Sensor placement for lifetime maximization in monitoring oil pipelines. In: Proceedings of the 1st ACM/IEEE International Conference on Cyber-physical Systems. Stockholm: ACM, 2010, 61–68
5 W B He, X Liu, H Nguyen, et al.A cluster-based protocol to enforce integrity and preserve privacy in data aggregation. In: Proceedings of IEEE International Conference on Distributed Computing Systems Workshops. Montreal: IEEE, 2009, 109–118
6 J Lee, K G Shin. Development and use of a new task model for cyber-physical systems: A real-time scheduling perspective. Journal of Systems and Software, 2017, 126(4): 45–56
7 F Zhang, K Szwaykowska, W Wolf, et al.Task scheduling for control oriented requirements for cyber-physical systems. In: Proceedings of IEEE Real-Time Systems Symposium. Barcelona: IEEE, 2008, 47–56
8 J Kim, K Lakshmanan, R Rajkumar. Rhythmic tasks: A new task model with continually varying periods for cyber-physical systems. In: Proceedings of International Conference on Cyber-Physical Systems. Beijing: IEEE, 2012, 55–64
9 J Zhang, X D Yang, H B Fan. An improved real-time task preemptive scheduling in cyber-physical systems. In: Proceedings of Chinese Control and Decision Conference. Chongqing, 2017, 5843–5848
10 C Y, Liu L C Zhang. Dynamic multi-priority scheduling for cyber-physical systems. Computer Science, 2015, 42(1): 28–32 (in Chinese)
11 S Adyanthaya, M, Geilen T Basten, et al.Fast multiprocessor scheduling with fixed task binding of large scale industrial cyber physical systems. In: Proceedings of Euromicro Conference on Digital System Design. Los Alamitos: IEEE, 2013, 979–988
12 B H Zhou, D P Yao. Research on reserved real-time scheduling approach for cyber and physical system. In: Proceedings of International Conference on Intelligent Networks and Intelligent Systems. Shenyang: IEEE, 2013, 62–65
13 C Fu, P Wu, M Li, et al.Real-time data retrieval with multiple availability intervals in CPS under freshness constraints. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2018, 37(11): 2743–2754
14 Z Ning, W Hou, X Hu, et al.A cloud-supported CPS approach to control decision of process manufacturing: 3D ONoC. In: Proceedings of the 13th IEEE Conference on Automation Science and Engineering. Xi’an: IEEE, 2017, 458–463
15 J Liu, F Zhao, X Liu, et al.Challenges towards elastic power management in internet data centers. In: Proceedings of the 29th IEEE International Conference on Distributed Computing Systems Workshops. Montreal: IEEE, 2009, 65–72
16 L Paroliniy, N Toliaz, B Sinopoliy, et al.A cyber-physical systems approach to energy management in data centers. In: Proceedings of the 1st ACM/IEEE International Conference on Cyber-physical Systems. Stockholm: ACM, 2010, 168–177
17 K, Ousterhout P, Wendell M Zaharia, et al.Sparrow: Distributed, low latency scheduling. In: Proceedings of the 24th ACM Symposium on Operating Systems Principle. Farmington: ACM, 2013, 69–84
18 C Delimitrou, C Kozyrakis. Quasar: Resource-efficient and QoS-aware cluster management. ACM SIGPLAN Notices, 2014, 49(4): 127–144
19 C Delimitrou, C Kozyrakis. Paragon: QoS-aware scheduling for heterogeneous datacenters. ACM SIGPLAN Notices, 2013, 48(4): 77–88
20 M T Higuera-Toledano, J L Risco-Martin, P Arroba, et al.Green adaptation of real-time web services for industrial CPS within a cloud environment. IEEE Transactions on Industrial Informatics, 2017, 13(3): 1249–1256
21 G Yildirim, Y. Tatar, Simplified agent-based resource sharing approach for WSN-WSN Interaction in IoT/CPS projects. IEEE ACCESS, 2018, 6: 78077–78091
22 M Armbrust, A Fox, R Griffith, et al.A view of cloud computing. Communications of the ACM, 2010, 53(4): 50–58
23 Z W Xu. Cloud-sea computing systems: Towards thousand-fold improvement in performance per watt for the coming zettabyte era. Journal of Computer Science and Technology, 2014, 29(2): 177–181
24 L M Vaquero, L Rodero-Merino . Finding your way in the fog: Towards a comprehensive definition of fog computing. Computer Communication Review, 2014, 44(5): 27–32
25 S P, Singh A Nayyar, R Kumar, et al.Fog computing: From architecture to edge computing and big data processing. Journal of Supercomputing, 2019, 75(4): 2070–2105
26 J Pereira, L Ricardo, M Luís, , et al.Assessing the reliability of fog computing for smart mobility applications in VANETs. Future Generation Computer Systems, 2019, 94: 317–332
27 K K Gai, M K Qiu, H Zhao, et al.Dynamic energy-aware cloudlet-based mobile cloud computing model for green computing. Journal of Network and Computer Applications, 2016, 59(c): 46–54
28 M T, Beck M Werner, S, Feldet al.Mobile edge computing: A taxonomy. In: Proceedings of the 6th International Conference on Advances in Future Internet. Lisbon, 2014, 48–54
29 F Zhang, J Ge, C Wong, et al.Online learning offloading framework for heterogeneous mobile edge computing system. Journal of Parallel and Distributed Computing, 2019, 128: 167–183
30 S G Wang, Y L Zhao, J L Xu, et al.Edge server placement in mobile edge computing. Journal of Parallel and Distributed Computing, 2019, 127: 160–168
31 T V, Do N H Do, H T Nguyen, et al.Comparison of scheduling algorithms for multiple mobile computing edge clouds. Simulation Modelling Practice and Theory, 2019, 93: 104–118
32 W S Shi, J Cao, Q Zhang, et al.Edge computing: Vision and challenges. IEEE Internet of Things Journal, 2016, 3(5): 637–646
33 T H J Uhlemann, C Lehmann, R Steinhilper. The digital twin: Realizing the cyber-physical production system for Industry 4.0. Procedia CIRP, 2017, 61: 335–340
34 S Yun, J H Park, W T Kim. Data-centric middleware based digital twin platform for dependable cyber-physical systems. In: Proceedings of the 9th International Conference on Ubiquitous and Future Networks. Milan: IEEE, 2017, 922–926
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