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

TL;DR

This paper introduces a novel quantum neural network model with adaptive architecture optimization for improved cloud workload prediction, significantly outperforming existing methods in accuracy and efficiency.

Contribution

The work presents a comprehensive adaptive architecture optimization algorithm integrated into a quantum neural network for the first time in cloud workload prediction.

Findings

Achieved up to 93.40% reduction in prediction error.

Outperformed seven state-of-the-art methods across four datasets.

Demonstrated the effectiveness of quantum adaptive modulation and size-adaptive recombination.

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.