随着新一代信息技术的发展，许多传统工厂开始向智能工厂转型。如何对智能工厂中海量数据进行处理，从而提高工厂的生产效率仍然是一个严峻的问题。基于智能工厂的特性提出适用于智能工厂的雾计算框架，使用Kubernetes对容器化的智能工厂应用进行自动化部署。并且提出基于遗传算法改进的区间划分遗传调度算法（interval division genetic scheduling arithmetic，IDGSA）对智能工厂中的容器应用进行调度分配。仿真实验表明，与Kubernetes缺省的调度算法相比，IDGSA算法可使数据处理时间减少50%，雾计算资源使用率提高达60%；与传统的遗传算法相比，在迭代次数更少的情况下，可使数据处理时间减少7%，雾计算资源的使用率提高9%。
With the development of next-generation information technology, many traditional factories have begun to transform into smart factories. How to deal with the massive data in smart factories and improve the production efficiency of the factory is still a serious problem. In this paper, based on the characteristics of smart factories, a fog computing framework for smart factories is proposed, and Kubernetes is used to automate the deployment of containerized smart factory applications.And an improved genetic algorithm based interval division genetic algorithm IDGSA (interval division genetic scheduling arithmetic) is proposed to dispatch and allocate container applications in smart factories. Simulation experiments show that compared with Kubernetes' default scheduling algorithm, IDGSA algorithm can reduce data processing time by 50% and increase the utilization rate of fog computing resources by 60%. Compared with traditional genetic algorithms, it has fewer iterations. In this case, the processing time of the data can be reduced by 7%, and the utilization rate of the fog computing resource can be increased by 9%.
[1] Yang Y, Luo X L, Chu X L, et al. Fog-enabled intelligent IoT systems[M]. Cham, Switzerland:Springer, 2019:29-31.
[2] Chiang M, Zhang T. Fog and IoT:an overview of research opportunities[J]. IEEE Internet of Things Journal, 2016, 3(6):854-864.
[3] Mourtzis D, Vlachou E, Milas N. Industrial big data as a result of IoT adoption in manufacturing[J]. Procedia CIRP, 2016, 55:290-295.
[4] Gazis V, Leonardi A, Mathioudakis K, et al. Components of fog computing in an industrial internet of things context[C]//2015 12th Annual IEEE International Conference on Sensing, Communication, and Networking-Workshops (SECON Workshops). Seattle, WA,USA:IEEE, 2015:1-6.
[5] Fazio M, Celesti A, Ranjan R, et al. Open issues in scheduling microservices in the cloud[J]. IEEE Cloud Computing, 2016, 3(5):81-88.
[6] Chen N X, Yang Y, Zhang T, et al. Fog as a service technology[J]. IEEE Communications Magazine, 2018, 56(11):95-101.
[7] Amaral M, Polo J, Carrera D, et al. Performance evaluation of microservices architectures using containers[C]//2015 IEEE 14th International Symposium on Network Computing and Applications. Cambridge,MA,USA:IEEE, 2015:27-34.
[8] Ha J, Kim J, Park H, et al. A web-based service deployment method to edge devices in smart factory exploiting Docker[C]//2017 International Conference on Information and Communication Technology Convergence (ICTC). Jeju, Korea(South):IEEE, 2017:708-710.
[9] Peinl R, Holzschuher F, Pfitzer F. Docker cluster management for the cloud:survey results and own solution[J]. Journal of Grid Computing, 2016, 14(2):265-282.
[10] Kubernetes. Production-grade container orchestration[EB/OL]. (2020-03-13)[2020-3-13]. https://kubernetes.io/.
[11] Skarlat O, Schulte S, Borkowski M,et al. Resource provisioning for IoT services in the fog[C]//2016 IEEE 9th International Conference on Service-Oviented Computing and Applications(SOCA),2016:32-39.
[12] Yin L X, Luo J, Luo H B. Tasks scheduling and resource allocation in fog computing based on containers for smart manufacture[J]. IEEE Transactions on Industrial Informatics, 2018, 14(10):4712-4721.
[13] Gedawy H, Habak K, Harras K, et al. An energy-aware IoT femtocloud system[C]//2018 IEEE International Conference on Edge Computing (EDGE).San Francisco,CA,USA:IEEE, 2018:58-65.
[14] Tayeb S, Latifi S, Kim Y. A survey on IoT communication and computation frameworks:An industrial perspective[C]//2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC). Las Vegas,NV,USA:IEEE, 2017:1-6.
[15] Sengupta J, Ruj S,Bit S D.A secure fog based architecture for industrial internet of things and industry 4.0[J]. IEEE Transactions on Industrial Informatics,2021,17(4):2316-2324.
[16] Wei G, Vasilakos A V, Zheng Y, et al. A game-theoretic method of fair resource allocation for cloud computing services[J]. The Journal of Supercomputing, 2010, 54(2):252-269.
[17] Zhan Z H, Liu X F, Gong Y J, et al. Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches[J]. ACM Computing Surveys, 2015, 47(4):1-33.
[18] Okay F Y, Ozdemir S. A fog computing based smart grid model[C]//2016 International Symposium on Networks, Computers and Communications (ISNCC). Yasmine Hammamet,Tunisia:IEEE, 2016:1-6.
[19] Yang Y. Multi-tier computing networks for intelligent IoT[J].Nature Electronics, 2019,2(1):4-5.
[20] Weaveworks. Microservices Demo:Sock Shop[EB/OL]. (2019-06-17)[2020-03-13]. https://microservices-demo.github.io/.
[21] Menasce D A. TPC-W:a benchmark for e-commerce[J].IEEE Internet Computing,2002, 6(3):83-87.
