期刊
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
卷 32, 期 12, 页码 2893-2905出版社
IEEE COMPUTER SOC
DOI: 10.1109/TPDS.2021.3079341
关键词
Qubit; Predictive models; Neural networks; Hidden Markov models; Prediction algorithms; Logic gates; Cloud computing; Cloud computing; differential evolution; quantum neural network; workload forecasting
资金
- National Institute of Technology, Kurukshetra, India
This work introduces a novel EQNN-based workload prediction model for Cloud datacenters, utilizing quantum computing efficiency and SB-ADE algorithm for optimization. Results show that the quantum neural network approach substantially improves prediction accuracy.
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 percent over the existing approaches.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据