A Comprehensively Adaptive Architectural Optimization-Ingrained Quantum Neural Network Model for Cloud Workloads...

Area of Science:

• Cloud Computing
• Artificial Intelligence
• Quantum Computing

Background:

• Accurate workload prediction and resource reservation are vital for dynamic cloud services.
• Traditional neural networks struggle with high-dimensional, dynamic workloads and sudden demand changes.
• Limited optimization in conventional models leads to inefficiencies in resource management.

Purpose of the Study:

• To propose a novel quantum neural network model for enhanced cloud workload prediction.
• To address the limitations of traditional models in handling diverse and dynamic cloud environments.
• To improve the accuracy and efficiency of resource management in cloud services.

Main Methods:

• Introduced a Comprehensively Adaptive Quantum Neural Network (CA-QNN) model.
• Utilized quantum computing principles, converting workload data into qubits for processing.
• Implemented a comprehensive architecture optimization algorithm with quantum adaptive modulation (QAM) and size-adaptive recombination.
• Employed qubit neurons with controlled not-gated activation functions for pattern recognition.

Main Results:

• CA-QNN demonstrated superior prediction accuracy on heterogeneous cloud workload datasets.
• Achieved significant error reduction, up to 93.40% compared to deep learning models.
• Reduced prediction errors by up to 91.27% compared to existing QNN-based approaches.
• Outperformed seven state-of-the-art methods in workload prediction tasks.

Conclusions:

• The CA-QNN model offers a significant advancement in cloud workload prediction and resource management.
• Quantum computing integration provides enhanced capabilities for handling complex and dynamic workloads.
• The proposed model leads to substantial improvements in prediction accuracy and efficiency for cloud services.