Design and Optimization of Scalable Edge Cloud Integrated Systems for Intelligent, Secure, and High-Availability Applications - Shaik, Babulal (International Journal of AI, BigData, Computational and Management Studies, 2026)
The surge of apps requesting real-time answers and generating massive amounts of data quickly led developers to integrate edge and cloud infrastructures - such a hybrid helps to carry out live processing while having access to flexible resources. However, the mixing of edge and cloud is beneficial but at the same time it faces challenges with smooth scaling, efficient power management, security, and availability of services over unstable, constantly changing networks. Our method presents a unified recipe for creating and optimizing large-scale edge-cloud infrastructures which are smartly routed for tasks, controlled in layers of resources, accompanied by AI-inferred insights - resulting in an improved task distribution, forecast-based rescaling, and preemptive fault detection. Security is ensured by diminished authentication procedures, encrypted communication channels, and quick anomaly detection - ideal for energy-constrained low-edge devices; uptime is kept at a high level due to deployment strategies that are backup-ready and quick recovery methods in case of failure. Experiments reveal improvements in response time, volume handled, robustness to stress, and safety levels compared to the older all-cloud models or fixed-edge layouts - thus, the real potential of this model for sophisticated, secure, and continuously available apps even with a rise in demands is ​‍​‌‍​‍‌proven.