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

2025-07-11

AI Summary

Cloud workloads exhibit frequent abrupt changes, high dimensionality, and complex temporal patterns, leading to poor prediction accuracy and generalization of conventional deep learning models. To address this, we propose a Variant Quantum Neural Network (VQNN) with fully adaptive structural optimization. VQNN introduces a novel co-learning mechanism for architecture and parameters, integrating qubit-based neurons, controlled-NOT gate activation functions, qubit-vector parameter learning, and structured evolutionary training. It further incorporates quantum-adaptive modulation and scale-adaptive reconfiguration strategies to effectively model abrupt workload dynamics. Evaluated on four heterogeneous cloud workload datasets, VQNN consistently outperforms seven state-of-the-art methods. It reduces prediction error by up to 93.40% compared to the best conventional deep learning model and by up to 91.27% relative to the strongest existing quantum neural network. These results demonstrate substantial improvements in both prediction accuracy and robustness under highly dynamic cloud environments.

Technology Category

• Machine Learning: Quantum Machine Learning
• Computer Vision: Adversarial Attacks & Robustness
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Accurate workload prediction and advanced resource reservation are indispensably crucial for managing dynamic cloud services. Traditional neural networks and deep learning models frequently encounter challenges with diverse, high-dimensional workloads, especially during sudden resource demand changes, leading to inefficiencies. This issue arises from their limited optimization during training, relying only on parametric (inter-connection weights) adjustments using conventional algorithms. To address this issue, this work proposes a novel Comprehensively Adaptive Architectural Optimization-based Variable Quantum Neural Network (CA-QNN), which combines the efficiency of quantum computing with complete structural and qubit vector parametric learning. The model converts workload data into qubits, processed through qubit neurons with Controlled NOT-gated activation functions for intuitive pattern recognition. In addition, a comprehensive architecture optimization algorithm for networks is introduced to facilitate the learning and propagation of the structure and parametric values in variable-sized QNNs. This algorithm incorporates quantum adaptive modulation and size-adaptive recombination during training process. The performance of CA-QNN model is thoroughly investigated against seven state-of-the-art methods across four benchmark datasets of heterogeneous cloud workloads. The proposed model demonstrates superior prediction accuracy, reducing prediction errors by up to 93.40% and 91.27% compared to existing deep learning and QNN-based approaches.

Problem

• Improving cloud workload prediction accuracy
• Overcoming inefficiencies in traditional neural networks
• Enhancing resource reservation for dynamic cloud services

Innovation

• Quantum neural network with architectural optimization
• Qubit data conversion and CNOT-gated activation
• Quantum adaptive modulation during training