ConfidenceQ: a Novel Confidence-Weighted Q-Learning Approach for Optimal Resource Allocation in a Multi-Broker Collaborated Cloud Systems

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Key Points

This research aims to develop and evaluate a novel confidence-weighted Q-learning algorithm for enhancing resource allocation in cloud systems.
Developed a confidence-weighted Q-learning approach for resource allocation.
Implemented the algorithm in a multi-broker collaborated cloud system.
Analyzed the effects of the new approach on resource efficiency and allocation outcomes.
The ConfidenceQ approach significantly improved resource allocation efficiency compared to traditional methods.
Demonstrated better performance metrics in real-time cloud environments, with a 15% increase in resource utilization.
Achieved lower latency and higher throughput with p<0.05, indicating a statistically significant improvement.

Evaluates a new Q-learning method for resource allocation in cloud systems, suggesting improved efficiency.

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Chaitra et al. (2026) studied this question.

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