Edge-Cloud Orchestration Strategies for Scalable Industrial Automation Systems

  • Authors

    • Dr. Ali Reza Professor, University of Tehran, Iran. Author

    DOI:

    https://doi.org/10.67228/3142788X/IJMLPA-2022PII9B8H

    Published 08-05-2022

  • Edge Computing, Cloud Computing, Industrial Automation, Orchestration Strategies, Industry 4.0, IoT, Real-Time Systems, Cyber-Physical Systems, Distributed Systems, Resource Allocation, Latency Optimization, Smart Manufacturing

    Issue

    Vol. 5 No. 2 (2022)

    Section

    Articles

    How to Cite

    [1]
    A. Reza, “Edge-Cloud Orchestration Strategies for Scalable Industrial Automation Systems”, IJMLPA, vol. 5, no. 2, pp. 01–14, Aug. 2022, doi: 10.67228/3142788X/IJMLPA-2022PII9B8H.

  • Abstract

    Industrial automation systems are undergoing a rapid transformation driven by the convergence of edge and cloud computing under the umbrella of Industry 4.0. These systems demand scalable, resilient, and low-latency computational infrastructures to support data-intensive and time-critical tasks. Traditional centralized cloud architectures often fall short in addressing the latency and bandwidth requirements of modern industrial environments, while edge-only solutions may lack the scalability and global coordination needed for complex workloads. This paper presents a comprehensive study of edge-cloud orchestration strategies tailored for scalable industrial automation systems. We explore dynamic workload allocation methods, latency-aware orchestration, and resource optimization techniques that enable seamless integration between edge and cloud resources. Through an in-depth analysis of architectural models, real-world use cases, and orchestration frameworks, we identify key design patterns and challenges. Our findings reveal that intelligent orchestration can significantly enhance operational efficiency, system scalability, and responsiveness in industrial settings. We also outline the open research areas and future directions toward fully autonomous and self-optimizing industrial infrastructures.

  • References

    [1] Bonomi, F., Milito, R., Zhu, J., & Addepalli, S. (2012). Fog computing and its role in the internet of things. Proceedings of the first edition of the MCC workshop on Mobile cloud computing, 13–16.

    [2] Satyanarayanan, M. (2017). The emergence of edge computing. Computer, 50(1), 30–39.

    [3] Zhou, Z., Chen, X., Li, E., Zeng, L., Luo, K., & Zhang, J. (2019). Edge intelligence: Paving the last mile of artificial intelligence with edge computing. Proceedings of the IEEE, 107(8), 1738–1762.

    [4] Wang, S., Zhang, X., Zhang, Y., Wang, L., Yang, J., & Wang, W. (2017). A survey on mobile edge networks: Convergence of computing, caching and communications. IEEE Access, 5, 6757–6779.

    [5] Li, Y., & Chen, M. (2018). Software-defined network function virtualization: A survey. IEEE Access, 3, 2542–2553.

    [6] Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016). Edge computing: Vision and challenges. IEEE Internet of Things Journal, 3(5), 637–646.

    [7] Premsankar, G., Di Francesco, M., & Taleb, T. (2018). Edge computing for the Internet of Things: A case study. IEEE Internet of Things Journal, 5(2), 1275–1284.

    [8] Gupta, H., Dastjerdi, A. V., Ghosh, S. K., & Buyya, R. (2017). iFogSim: A toolkit for modeling and simulation of resource management techniques in the Internet of Things, Edge and Fog computing environments. Software: Practice and Experience, 47(9), 1275–1296.

    [9] Morabito, R., Cozzolino, V., Ding, A. Y., Beijar, N., & Ott, J. (2018). Consolidate IoT edge computing with lightweight virtualization. IEEE Network, 32(1), 102–111.

    [10] Taleb, T., Samdanis, K., Mada, B., Flinck, H., Dutta, S., & Sabella, D. (2017). On multi-access edge computing: A survey of the emerging 5G network edge cloud architecture and orchestration. IEEE Communications Surveys & Tutorials, 19(3), 1657–1681.

    [11] Abbas, N., Zhang, Y., Taherkordi, A., & Skeie, T. (2018). Mobile edge computing: A survey. IEEE Internet of Things Journal, 5(1), 450–465.

    [12] OpenFog Consortium. (2017). OpenFog Reference Architecture for Fog Computing. OpenFog Consortium Architecture Working Group.

    [13] Industrial Internet Consortium. (2019). Industrial Internet Reference Architecture. IIC Publication.

    [14] Wang, Y., Yang, W., & Wang, L. (2020). Intent-based networking and orchestration: A new paradigm for industrial IoT. IEEE Internet of Things Journal, 7(6), 4958–4968.

    [15] Singh, M., Rajan, M. A., Shivraj, V. L., & Balamuralidhar, P. (2015). Secure MQTT for internet of things (IoT). IEEE International Conference on Communication Systems and Network Technologies, 746–751.

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