论文标题
一种用于自适应预测云工作负载的量子方法
A Quantum Approach Towards the Adaptive Prediction of Cloud Workloads
论文作者
论文摘要
这项工作介绍了基于云数据中心的基于新颖的进化量子神经网络（EQNN）的工作负载预测模型。它通过将工作负载信息编码到量子位并通过网络传播，从而利用量子计算的计算效率来估算工作负载或资源需求，以增强的准确性。受控的不（C-NOT）门的旋转和反向旋转效应在隐藏层和输出层上发挥激活功能，以调节量子重量。此外，开发了一种自我平衡的自适应差异进化（SB-ade）算法来优化量子网络权重。 EQNN预测模型的准确性经过广泛的评估，并与七个最先进的方法相比，使用三种不同类别的八个现实世界基准数据集进行了比较。实验结果表明，量子方法在进化神经网络中的使用基本上提高了预测准确性，高达91.6％。
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.
