Network Programming and Microservices: Building Scalable AI-Driven Distributed Systems for Real-Time Data Processing

Abstract

This paper explores the integration of network programming and microservices architecture to build scalable, AI-driven distributed systems for real-time data processing. As artificial intelligence becomes increasingly crucial for real-time decision-making in industries like healthcare, finance, and e-commerce, there is a growing need for systems that can process vast amounts of data efficiently while ensuring scalability and low latency. Network programming techniques are foundational to distributed systems, enabling seamless communication between services. Meanwhile, microservices provide a modular approach that supports scalability and flexibility, essential for AI applications. The paper discusses the role of these technologies in building AI-powered distributed systems, challenges related to network latency, data consistency, and fault tolerance, and real-world applications across industries. Additionally, it delves into future trends such as edge computing and automated scaling in the context of AI-driven distributed systems.