STREAM PROCESSING IN DECENTRALIZED ARCHITECTURES: CHALLENGES AND ADAPTIVE SOLUTIONS ACROSS CLOUD, FOG, AND EDGE

In recent years, the rapid development of data-driven applications has posed significant challenges for data computation in different domains. Handling and processing continuous data streams have become essential for building data-driven organizations, which places a high burden on traditional computing. As a centralized method, cloud computing often struggles with application latency, mainly because of geographic distance and network bandwidth challenges . The increasing scale and complexity of data, characterized by high volume, velocity, and variety, demand computational infrastructures that are powerful, adaptive, and efficient in terms of pro- cessing. Fog and Edge computing are two decentralized network solutions that move the computation closer to the data source, lowering network traffic while improving the response time. Edge computing performs computations within IoT devices, resulting in real-time data processing and subsequently transferring less time-critical data to the cloud. In contrast, Fog Computing utilizes fog nodes with high computational power for data processing and storage. These nodes are within the same local network, and a decentralized solution is a better choice when working with a large number of IoT devices, the need for local computational power, and storage. Both fog and edge computing rely on cloud infrastructure for long-term data storage and larger computations. This study provides a comprehensive comparative analysis of the Fog, Edge, and Cloud computing paradigms, with a particular focus on their applicability to real-time data stream processing. to determine their strengths and ideal use cases in a table and to showcase their advantages and disadvantages in stream processing