Exploring AI-Driven Cloud-Edge Orchestration for IoT Applications
DOI:
https://doi.org/10.32628/CSEIT239072Keywords:
Artificial Intelligence, Internet of Things, Cloud-Edge Orchestration, Resource Management, Real-Time Analytics.Abstract
The integration of Artificial Intelligence (AI), cloud computing, and edge computing has transformed the Internet of Things (IoT) ecosystem by addressing critical challenges such as latency, scalability, resource management, and fault tolerance. IoT applications generate massive amounts of data, requiring real-time decision-making and efficient resource allocation, which traditional cloud-centric architectures often fail to deliver due to inherent latency and bandwidth limitations. Edge computing, as a decentralized extension of the cloud, brings computation closer to the data source, reducing latency and enabling real-time analytics. However, the dynamic and heterogeneous nature of cloud-edge systems presents significant orchestration challenges. This paper explores how AI-driven optimization enhances cloud-edge orchestration by improving task scheduling, predictive analytics, and data processing. AI models, such as reinforcement learning, neural networks, and bio-inspired algorithms, enable dynamic workload distribution, proactive resource allocation, and energy-efficient operations, thereby improving system reliability and scalability. Furthermore, the study highlights innovative integration models, including hierarchical, collaborative, and federated approaches, which cater to diverse IoT requirements by balancing the computational power of the cloud with the agility of edge nodes. Through an extensive review of recent research, this study identifies key challenges in data privacy, scalability, real-time orchestration, and fault tolerance, while also exploring novel opportunities, such as privacy-aware federated learning frameworks, lightweight AI models for edge devices, blockchain for fault resilience, and bio-inspired energy optimization techniques. Real-world use cases in domains such as smart manufacturing, autonomous vehicles, and healthcare demonstrate the practical benefits of AI-powered orchestration, showcasing reductions in latency and energy consumption alongside improvements in system scalability and responsiveness.
References
[1] M. Abd Elaziz, L. Abualigah, and I. Attiya, “Advanced optimization technique for scheduling IoT tasks in cloud-fog computing environments,” Future Gener. Comput. Syst., vol. 124, pp. 142–154, Nov. 2021. [2] M. Barcelo, A. Correa, J. Llorca, A. M. Tulino, J. L. Vicario, and A. Morell, “IoT-cloud service optimization in next generation smart environments,” IEEE J. Sel. Areas Commun., vol. 34, no. 12, pp. 4077–4090, Dec. 2016. [3] G. Somani, M. S. Gaur, D. Sanghi, M. Conti, M. Rajarajan, and R. Buyya, “Combating DDoS attacks in the cloud: Requirements, trends, and future directions,” IEEE Cloud Comput., vol. 4, no. 1, pp. 22–32, Jan. 2017. [4] G. A. Gravvanis, J. P. Morrison, D. Petcu, T. Lynn, and C. K. Filelis-Papadopoulos, “Special Issue: Recent trends in cloud computing,” Future Gener. Comput. Syst., vol. 79, pp. 700–702, Feb. 2018. [5] H. Shahzad, A. Sanaullah, and M. Herbordt, “Survey and future trends for FPGA cloud architectures,” in 2021 IEEE High Performance Extreme Computing Conference (HPEC), Waltham, MA, USA, 2021. [6] S. Wallin, “Edge and cloud orchestration — same same but different (part 2 of 2),” avassa.io, 24-Mar-2022. . [7] A. Mijuskovic, A. Chiumento, R. Bemthuis, A. Aldea, and P. Havinga, “Resource management techniques for cloud/fog and edge computing: An evaluation framework and classification,” Sensors (Basel), vol. 21, no. 5, p. 1832, Mar. 2021. [8] M. S. Aslanpour, S. S. Gill, and A. N. Toosi, “Performance evaluation metrics for cloud, fog and edge computing: A review, taxonomy, benchmarks and standards for future research,” Internet of Things, vol. 12, no. 100273, p. 100273, Dec. 2020. [9] Z. Wen, R. Yang, P. Garraghan, T. Lin, J. Xu, and M. Rovatsos, “Fog orchestration for internet of things services,” IEEE Internet Comput., vol. 21, no. 2, pp. 16–24, Mar. 2017. [10] J.-S. Tsai, I.-H. Chuang, J.-J. Liu, Y.-H. Kuo, and W. Liao, “QoS-aware fog service orchestration for industrial internet of things,” IEEE Trans. Serv. Comput., vol. 15, no. 3, pp. 1265–1279, May 2022. [11] Z. Zhou, J. Feng, L. Tan, Y. He, and J. Gong, “An air-ground integration approach for mobile edge computing in IoT,” IEEE Commun. Mag., vol. 56, no. 8, pp. 40–47, Aug. 2018. [12] G. Fortino, “Keynote speech 1: Blockchain-enabled trust in edge-based internet of things architectures: State of the art and research challenges,” in 2021 Third International Conference on Blockchain Computing and Applications (BCCA), Tartu, Estonia, 2021. [13] H. J. Damsgaard, A. Ometov, and J. Nurmi, “Approximation opportunities in Edge computing hardware: A systematic literature review,” ACM Comput. Surv., Nov. 2022. [14] A. H. Sodhro and N. Zahid, “AI-enabled framework for fog computing driven E-healthcare applications,” Sensors (Basel), vol. 21, no. 23, p. 8039, Dec. 2021. [15] J. A. Ruiz-Vanoye et al., “Edge computing–Foundations and applications,” in AI, Edge and IoT-based Smart Agriculture, Elsevier, 2022, pp. 17–30. [16] G. Carvalho, B. Cabral, V. Pereira, and J. Bernardino, “Computation offloading in Edge Computing environments using Artificial Intelligence techniques,” Eng. Appl. Artif. Intell., vol. 95, no. 103840, p. 103840, Oct. 2020. [17] Y. Ma, X. Li, and J. Li, “An edge computing offload method based on NSGA-II for power internet of things,” Internet Things Cloud Comput., vol. 9, no. 1, p. 1, 2021. [18] G. Xie, Z. Wang, and Y. Liu, “Joint task partition and resource allocation for multiuser cooperative mobile edge computing,” Wirel. Commun. Mob. Comput., vol. 2022, pp. 1–13, Jul. 2022. [19] J. Wang, C. Cao, J. Wang, K. Lu, A. Jukan, and W. Zhao, “Optimal task allocation and coding design for secure edge computing with heterogeneous edge devices,” IEEE Trans. Cloud Comput., vol. 10, no. 4, pp. 2817–2833, Oct. 2022. [20] A. Orive, A. Agirre, H.-L. Truong, I. Sarachaga, and M. Marcos, “Quality of service aware orchestration for cloud-Edge continuum applications,” Sensors (Basel), vol. 22, no. 5, p. 1755, Feb. 2022. [21] Y. Yazid, I. Ez-Zazi, A. Guerrero-González, A. El Oualkadi, and M. Arioua, “UAV-enabled mobile edge-computing for IoT based on AI: A comprehensive review,” Drones, vol. 5, no. 4, p. 148, Dec. 2021. [22] H. Singh, S. Tyagi, P. Kumar, S. S. Gill, and R. Buyya, “Metaheuristics for scheduling of heterogeneous tasks in cloud computing environments: Analysis, performance evaluation, and future directions,” Simul. Model. Pract. Theory, vol. 111, no. 102353, p. 102353, Sep. 2021. [23] S. H. H. Madni, M. S. A. Latiff, Y. Coulibaly, and S. M. Abdulhamid, “Resource scheduling for infrastructure as a service (IaaS) in cloud computing: Challenges and opportunities,” J. Netw. Comput. Appl., vol. 68, pp. 173–200, Jun. 2016. [24] M. Khashei and F. Chahkoutahi, “Electricity demand forecasting using fuzzy hybrid intelligence-based seasonal models,” J. Model. Manag., vol. ahead-of-print, no. ahead-of-print, Jul. 2021.
Downloads
Published
Issue
Section
License
Copyright (c) IJSRCSEIT
This work is licensed under a Creative Commons Attribution 4.0 International License.