AI-Orchestrated Cloud Pipelines with Microservices and Containerization for Sustainable Smart Mobility
DOI:
https://doi.org/10.15662/IJARCST.2023.0605005Keywords:
AI orchestration, Cloud pipelines, Microservices, Containerization, Smart mobility, Sustainable computing, Resource optimization, Intelligent transport systems, Real-time decision making, Cloud-native architectureAbstract
The growing demand for sustainable smart mobility requires intelligent, scalable, and energy-efficient computing infrastructures. Conventional monolithic architectures often fail to meet the flexibility and performance requirements of modern mobility ecosystems. This paper proposes an AI-orchestrated cloud pipeline powered by microservices and containerization to support sustainable smart mobility solutions. The framework leverages artificial intelligence to dynamically allocate resources, optimize workload distribution, and manage data pipelines across heterogeneous environments. By decoupling functionalities into containerized microservices, the system ensures modularity, fault tolerance, and rapid scalability. AI-driven orchestration enhances system adaptability, enabling real-time decision-making for applications such as traffic flow optimization, multimodal transport integration, and energy-aware route planning. Experimental evaluation demonstrates that the proposed architecture achieves reduced energy consumption, improved latency performance, and enhanced scalability compared to traditional cloud-based mobility systems. This research highlights the synergy between AI, cloud-native microservices, and containerization technologies in enabling reliable, sustainable, and intelligent smart mobility infrastructures.
References
1. Internet of Things and AI for Secure & Sustainable Green Mobility: a multimodal data fusion approach — 20% shorter travel, 15% energy saving, 10% CO₂ cut .
2. Manda, P. (2023). Migrating Oracle Databases to the Cloud: Best Practices for Performance, Uptime, and Risk Mitigation. International Journal of Humanities and Information Technology, 5(02), 1-7.
3. R. Sugumar, A. Rengarajan and C. Jayakumar, Design a Weight Based Sorting Distortion Algorithm for Privacy Preserving Data Mining, Middle-East Journal of Scientific Research 23 (3): 405-412, 2015.
4. S. Devaraju, HR Information Systems Integration Patterns, Independently Published, ISBN: 979-8330637850, DOI: 10.5281/ZENODO.14295926, 2021.
5. T. Yuan, S. Sah, T. Ananthanarayana, C. Zhang, A. Bhat, S. Gandhi, and R. Ptucha. 2019. Large scale sign language interpretation. In Proceedings of the 14th IEEE International Conference on Automatic Face Gesture Recognition (FG’19). 1–5.
6. Sugumar, R. (2022). Estimation of Social Distance for COVID19 Prevention using K-Nearest Neighbor Algorithm through deep learning. IEEE 2 (2):1-6.
7. Amuda, K. K., Kumbum, P. K., Adari, V. K., Chunduru, V. K., & Gonepally, S. (2021). Performance evaluation of wireless sensor networks using the wireless power management method. Journal of Computer Science Applications and Information Technology, 6(1), 1–9. https://doi.org/10.15226/2474-9257/6/1/00151
8. Sugumar, R., Rengarajan, A. & Jayakumar, C. Trust based authentication technique for cluster based vehicular ad hoc networks (VANET). Wireless Netw 24, 373–382 (2018). https://doi.org/10.1007/s11276-016-1336-6
9. AI accelerates auto industry’s green shift — 25% energy savings and intelligent EV charging features .
10. Infosys “right cloud” framework — 30% carbon reduction & efficient data center footprint .
11. Green cloud practices: AI autoscaling, carbon tracking .
12. Cherukuri, Bangar Raju. "Microservices and containerization: Accelerating web development cycles." (2020).
13. Computational Sustainability overview and ITS context .
14. Devaraju, S., Katta, S., Donuru, A., & Devulapalli, H. Comparative Analysis of Enterprise HR Information System (HRIS) Platforms: Integration Architecture, Data Governance, and Digital Transformation Effectiveness in Workday, SAP SuccessFactors, Oracle HCM Cloud, and ADP Workforce Now.
15. Sangannagari, S. R. (2023). Smart Roofing Decisions: An AI-Based Recommender System Integrated into RoofNav. International Journal of Humanities and Information Technology, 5(02), 8-16.
16. Autonomy and Intelligence in the Computing Continuum: orchestration strategies.


