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Cloud computing

Machine learning

Researchers Develop Quantum-Inspired Framework for Energy-Efficient Cloud Computing

Researchers at Galgotias University have unveiled a novel Quantum-Inspired Hybrid Reinforcement Learning and Multi-Objective Optimization Framework (QHRMOF) designed to optimize task scheduling, dynamic load balancing, and server consolidation while minimizing power consumption and enhancing system performance. The framework integrates quantum-inspired evolutionary algorithms, hybrid deep reinforcement learning, and multi-objective optimization techniques

Machine learning

Machine Learning-based Resource Allocation Techniques in Cloud Computing Show Promising Results

A systematic literature review has analyzed and categorized existing resource allocation techniques in cloud computing environments, focusing on optimization strategies, heuristic algorithms, and machine learning-based approaches. The study, conducted by researchers at Maharshi Dayanand University, examined 100 research articles published between 2014 and 2025, identifying strengths and limitations of different

Control systems

Autonomous Industrial Cyber-Physical Systems: A New Paradigm Emerges

Researchers from Swinburne University of Technology have published a report on a new digitalized industrial automation reference architecture, Cloud-Fog Automation, which seeks to revolutionize the field of autonomous industrial cyber-physical systems (ICPS). This architecture represents a fundamental paradigm shift from traditional models, enabling seamless integration of physical processes with communication,
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