A Quantum Approach Towards the Adaptive Prediction of Cloud Workloads

TL;DR

This paper introduces a novel quantum neural network model for cloud workload prediction that leverages quantum computing principles and evolutionary algorithms to significantly improve prediction accuracy.

Contribution

It presents an innovative EQNN model utilizing quantum encoding and a new SB-ADE algorithm for optimizing qubit weights, outperforming existing methods.

Findings

Prediction accuracy improved up to 91.6%

Outperforms seven state-of-the-art methods

Validated on eight real-world datasets

Abstract

This work presents a novel Evolutionary Quantum Neural Network (EQNN) based workload prediction model for Cloud datacenter. It exploits the computational efficiency of quantum computing by encoding workload information into qubits and propagating this information through the network to estimate the workload or resource demands with enhanced accuracy proactively. The rotation and reverse rotation effects of the Controlled-NOT (C-NOT) gate serve activation function at the hidden and output layers to adjust the qubit weights. In addition, a Self Balanced Adaptive Differential Evolution (SB-ADE) algorithm is developed to optimize qubit network weights. The accuracy of the EQNN prediction model is extensively evaluated and compared with seven state-of-the-art methods using eight real world benchmark datasets of three different categories. Experimental results reveal that the use of the quantum approach to evolutionary neural network substantially improves the prediction accuracy up to 91.6% over the existing approaches.