Hybrid Fog-Cloud Architectures for Enhanced IoT Scalability and Performance

The amount of IoT devices are growing extremely fast and produce huge amount of data that call for highly efficient and high-performance computing architecture. Analysis shows that traditional cloud based IoT has high latency, limited bandwidth, poor processing efficiency and are unsuitable for real time applications such as smart city, healthcare, etc. Localizing data processing, reducing latency and reducing network traffic, the fog computing is limited in applying complex computation because the fog nodes need powerful architecture and so are expensive. This research proposes Hybrid Fed + Fog-Cloud Orchestration model, which combines the favorable low latency advantages provided by fog computing with the higher storage and analytics capacity provided by cloud computing. AI-driven task scheduling and dynamic resource allocation distribute workloads efficiently across IoT devices, fog nodes, and cloud servers. FL reduces the cloud dependency and helps to achieve security, privacy, and real time decision making without sharing raw data among the subjects. iFogSim, CloudSim, EdgeCloudSim are used to simulate the proposed model and they are evaluated basing their key performance metrics on latency, energy consumption, bandwidth, task completion rate. By demonstrating reduced computational delays, improved scalability, enhanced fault tolerance, it is indeed a good candidate for large scale IoT facilitation. Moreover, intelligent, adaptive hybrid architectures are developed in this research that facilitate efficiency, security, and sustainability in dynamic IoT environments.