Artificial Intelligence Frameworks for Intelligent Resource Management

Pedro Domingos

Artificial Intelligence Frameworks for Intelligent Resource Management

Author(s)	Pedro Domingos
Country	United States
Abstract	The increasing complexity of digital infrastructure has created a strong need for intelligent resource management frameworks capable of allocating computational, energy, storage, network, and human resources efficiently. Conventional resource-management approaches frequently depend on predefined rules and static optimization strategies, which may become ineffective when workloads, environmental conditions, and resource availability change dynamically. Artificial intelligence provides adaptive mechanisms through machine learning, deep learning, reinforcement learning, predictive analytics, and multi-agent intelligence that can continuously evaluate resource conditions and optimize allocation decisions. This study examines artificial intelligence frameworks for intelligent resource management, focusing on resource allocation, scheduling, provisioning, load balancing, task offloading, energy optimization, and adaptive decision-making. A qualitative descriptive research design was adopted through a structured review and conceptual analysis of relevant literature. Recent research indicates that AI-driven resource allocation can improve latency, cost, and energy efficiency in cloud environments, while edge and fog computing require additional attention to resource heterogeneity, limited capacity, workload variability, and real-time decision-making. The findings indicate that AI-based frameworks can provide more responsive and context-aware resource management than conventional static approaches. However, challenges involving computational overhead, explainability, data quality, model drift, security, scalability, and coordination across distributed environments remain important. The study concludes that effective AI resource management requires integrated frameworks combining predictive intelligence, adaptive optimization, continuous monitoring, and responsible governance.
Keywords	Artificial Intelligence, Intelligent Resource Management, Machine Learning, Resource Allocation, Resource Optimization, Reinforcement Learning, Cloud Computing, Edge Computing, Resource Scheduling, Adaptive Systems
Field	Engineering
Published In	Volume 1, Issue 3, May-June 2019
Published On	2019-05-19

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American International Journal of Advanced Research and Development

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Artificial Intelligence Frameworks for Intelligent Resource Management

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