Intelligent Self-Organized, Dynamic, and Adaptive Complex Distributed Systems

The proliferation of complex distributed systems increasingly characterizes the contemporary digital landscape. The challenge of managing complexity, heterogeneity, and communication is at the forefront of the new paradigms required for distributed computing, edge-friendly artificial intelligence (AI), and the integration of complex systems in research and practice. High-level abstractions are essential for enabling the efficient design, analysis, and deployment of innovative, scalable solutions for intricately coupled heterogeneous environments.

Distributed, self-organized, and adaptive ecosystems necessitate advancements in virtualization, software-defined networking (SDN), resource federation, generative AI, AI agents, and the further development of the "software-defined everything" (SDX) concept. Technologies such as swarm and edge intelligence enable resilience and efficiency in distributed applications, while advances in AI may favour self-organization, dynamic learning, and efficient resource management capabilities. The increasing autonomy and complexity driven by AI necessitate a firm focus on trustworthiness. For intelligent, self-organizing, and complex distributed systems to be adopted and function safely, particularly in critical applications, mechanisms for transparency and accountability are paramount, with explainability (XAI) and interpretability (IAI) being integral design considerations.

Topics of interest include, but are not limited to:

• Abstraction models, virtualization, and SDX paradigms for advanced distributed systems.
• Swarm-based architectures and algorithms for distributed intelligence.
• Edge-cloud continuum orchestration and resource federation.
• Distributed deployment automation and application management at scale.
• Intelligent platforms for distributed data management, multi-agent systems, and swarm intelligence.
• AI and generative AI frameworks for self-organizing systems.
• AI multi-agent distributed frameworks.
• End-to-end security, resiliency, and traceability in distributed environments across the edge-cloud continuum.
• Adaptive and emergent behavior through self-learning and dynamic reconfiguration.
• Explainable AI (XAI), interpretability (IAI), and accountability in distributed systems.
• Collaborative AI-assisted programming environments and tools for engineering complex, scalable, distributed, and adaptive systems.

Through this collection, we seek to bridge theoretical, engineering, and application-driven perspectives, advancing trustworthy and adaptive distributed systems across diverse domains. Interdisciplinary submissions from fields such as distributed computing, AI, generative AI, swarm intelligence, systems engineering, and industrial automation are encouraged.

Topic Editor Dr. Chandra Krintz is co-founder and Chief Scientist of AppScale Systems, Inc. The other Topic Editors declare no competing interests concerning the Research Topic subject.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
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• General Commentary
• Hypothesis and Theory
• Methods
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Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Review
• Systematic Review
• Technology and Code

Keywords: Complex Distributed Systems, Self-Organization, Adaptive Systems, Distributed Intelligence, Explainable AI (XAI)

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.