Enterprise Architecture for Real-Time Intelligence in Distributed Environments
DOI:
https://doi.org/10.15680/IJCTECE.2023.0604009Keywords:
enterprise architecture, real-time systems, distributed platforms, cloud-edge integration, scalability, system designAbstract
In the digital transformation, the contemporary businesses are becoming more dependent on real time intelligence to function well in the distributed, latency sensitive environments. This article introduces a novel enterprise architecture that smoothly incorporates single centralized cloud systems and distributed edge and on-premise systems. The architecture is made so as to maximize real-time decision-making by enabling organizations to adapt with flexibility to dynamic factors and at the same time remain scalable and responsive. The suggested design will consider a number of enterprise limitations, including network variability, data governance boundaries, cost optimization, and operational challenges of operating such systems. The main characteristics of the architecture are the strategic placement of workloads, life cycle coordination of models and rules deployed, and a high level of observability linking the effects of the distributed systems to centralised governance structures. This is a handy reference architecture that offers a systemic methodology to real-time intelligence to businesses on how to fulfill control, compliance, and sustainability of the system in the long run. The strategy is especially useful in cases when organizations aim at using the potential of distributed systems but do not want to sacrifice system integrity or efficiency.
References
1. S. Dhelim, T. Kechadi, L. Chen, N. Aung, H. Ning, and L. Atzori, “Edge-enabled metaverse: The convergence of metaverse and mobile edge computing,” arXiv preprint arXiv:2205.02764, 2022.
2. M. Xu, D. Niyato, J. Kang, Z. Xiong, C. Miao, and D. I. Kim, “Wireless edge-empowered metaverse: A learning-based incentive mechanism for virtual reality,” arXiv preprint arXiv:2111.03776, 2021.
3. Y. Sahni, J. Cao, L. Yang, and Y. Ji, “Multi-hop multi-task partial computation offloading in collaborative edge computing,” IEEE Transactions on Parallel and Distributed Systems, vol. 32, no. 5, pp. 1133–1145, 2020.
4. Z. Xu, S. Sinha, S. Harshil S, and U. Ramachandran, “Space-time vehicle tracking at the edge of the network,” in The Workshop on Hot Topics in Video Analytics and Intelligent Edges, 2019, pp. 15–20.
5. W.-S. Kim, “Edge computing server deployment technique for cloud vr-based multi-user metaverse content,” Journal of Korea Multimedia Society, vol. 24, no. 8, pp. 1090–1100, 2021.
6. Y. Xiong, Y. Sun, L. Xing, and Y. Huang, “Extend cloud to edge with kubeedge,” in IEEE/ACM Symposium on Edge Computing, 2018, pp. 373–377.
7. F. Al-Doghman, N. Moustafa, I. Khalil, Z. Tari, and A. Zomaya, “AI-enabled secure microservices in edge computing: Opportunities and challenges,” IEEE Transactions on Services Computing, 2022.
8. Y. Zhao, Y. Yin, and G. Gui, “Lightweight deep learning based intelligent edge surveillance techniques,” IEEE Transactions on Cognitive Communications and Networking, vol. 6, no. 4, pp. 1146–1154, 2020.
9. S. Liu, L. Liu, J. Tang, B. Yu, Y. Wang, and W. Shi, “Edge computing for autonomous driving: Opportunities and challenges,” Proceedings of the IEEE, vol. 107, no. 8, pp. 1697–1716, 2019.
10. J. Meng, H. Tan, C. Xu, W. Cao, L. Liu, and B. Li, “Dedas: Online task dispatching and scheduling with bandwidth constraint in edge computing,” in IEEE Conference on Computer Communications, 2019, pp. 2287–2295.
11. X. Lyu, W. Ni, H. Tian, R. P. Liu, X. Wang, G. B. Giannakis, and A. Paulraj, “Optimal schedule of mobile edge computing for internet of things using partial information,” IEEE Journal on Selected Areas in Communications, vol. 35, no. 11, pp. 2606–2615, 2017.
12. Z. Han, H. Tan, X.-Y. Li, S. H.-C. Jiang, Y. Li, and F. C. Lau, “Ondisc: Online latency-sensitive job dispatching and scheduling in heterogeneous edge-clouds,” IEEE/ACM Transactions on Networking, vol. 27, no. 6, pp. 2472–2485, 2019
