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Software Architecture Models for Intelligent Computing Systems

Author(s) Prof. Dr. Stefan Wagner
Country Germany
Abstract The rapid development of artificial intelligence, machine learning, cloud computing, Internet of Things, edge computing, distributed applications, and data-intensive platforms has transformed the requirements of modern software architecture. Intelligent computing systems must process large volumes of information, support adaptive decision-making, integrate heterogeneous technologies, and provide reliable services under continuously changing operational conditions. Conventional monolithic architectures may become difficult to scale and maintain when intelligent capabilities are incorporated into increasingly complex applications. Consequently, software architecture models have become important for organizing computational components, data flows, intelligence services, interfaces, security mechanisms, and infrastructure resources. This study examines software architecture models for intelligent computing systems and develops an integrated conceptual framework for designing scalable, adaptive, reliable, secure, and maintainable intelligent applications.
A conceptual and descriptive research design was adopted through thematic analysis of scholarly literature related to software architecture, intelligent systems, microservices, service-oriented architecture, cloud computing, edge computing, event-driven architectures, artificial intelligence, distributed systems, and software engineering. The analysis identifies modularity, scalability, interoperability, adaptability, observability, security, resilience, and efficient data management as important architectural requirements for intelligent computing systems. Monolithic architecture remains suitable for relatively simple systems with limited operational complexity, whereas modular and distributed approaches provide greater flexibility for large intelligent applications. Microservices architecture enables individual services to be independently developed, deployed, scaled, and updated, while event-driven architectures can support real-time intelligent processing. Cloud-edge architectures can distribute computation according to latency, bandwidth, and resource requirements. The proposed framework integrates architectural planning, component decomposition, data management, intelligence services, communication mechanisms, security, monitoring, deployment, and continuous architectural evolution.
The study further identifies challenges involving architectural complexity, distributed coordination, data consistency, latency, resource limitations, interoperability, security, model integration, technical debt, and governance. The findings demonstrate that no single architecture model is universally optimal for intelligent computing systems. Architectural decisions should instead be based on workload characteristics, intelligence requirements, scalability objectives, latency constraints, data sensitivity, operational context, and organizational capabilities. The study concludes that hybrid and modular architectures provide a practical foundation for intelligent computing systems because they combine the flexibility of distributed components with centralized governance and coordinated data management. Future research should investigate self-adaptive architectures, AI-driven architectural optimization, autonomous software components, intelligent edge-cloud orchestration, and architectures designed specifically for trustworthy and explainable artificial intelligence.
Keywords Software Architecture, Intelligent Computing Systems, Microservices, Distributed Systems, Cloud Computing, Edge Computing, Artificial Intelligence, Machine Learning, Event-Driven Architecture, Software Engineering
Field Engineering
Published In Volume 2, Issue 6, November-December 2020
Published On 2020-11-26
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