AI 中文总结
针对工业云资源管理问题，提出QB-HNN模型，结合量子力学与神经网络，经QB-BiO算法优化，在6个基准数据集上较LSTM、EQNN等方法降低预测误差最高36.36%，提升预测性能
AI 中文摘要
精确的工作负载预测与主动资源预留对工业云至关重要。然而，学习能力有限的传统机器学习（CML）模型往往无法预测资源需求突变的多样化高维工作负载，导致功耗过高及资源管理问题。在此背景下，本文提出一种新型带量子黑洞优化的哈达玛神经网络（QB-HNN），该模型结合量子力学的计算效率与神经网络（NN）的强学习能力。工作负载信息被转换为量子比特，通过由含哈达玛门激活函数的量子比特神经元构成的深度网络传播，以在QB-HNN模型中获取叠加态用于直观的模式学习。此外，引入新型量子黑洞双相优化（QB-BiO）算法训练并优化量子比特神经权重。通过6个涵盖3种异构类型云工作负载的基准数据集，将所提模型与5种最先进方法进行全面评估与对比。该模型对大范围工作负载的预测精度表明其性能优异，与现有基于LSTM和EQNN的预测方法相比，预测误差分别降低达36.36%和22.83%。
英文摘要
Accurate workload prediction and proactive resource reservation are crucial for industry clouds. However, the conventional machine learning (CML) models with limited learning capabilities often fail to predict diverse, high-dimensional workloads with sudden changes in resource demand, leading to excessive power consumption and resource management issues. In this context, this article proposes a novel Hadamard neural network with quantum blackhole (QB-HNN) optimization. This model combines the computational efficiency of quantum mechanics with the persuasive learning capability of neural networks (NNs). The workload information is transformed into qubits and propagated via a deep network of qubit neurons comprising a Hadamard-gated activation function to fetch superposition within the QB-HNN model for intuitive pattern learning. Furthermore, a novel quantum blackhole biphase optimization (QB-BiO) algorithm is introduced to train and optimize qubit neural weights. The performance of the proposed model is comprehensively evaluated and compared with five state-of-the-art approaches using six benchmark datasets of three heterogeneous varieties of cloud workloads. The prediction accuracy achieved for an extensive range of workloads confirms its influential performance by minimizing the prediction error up to 36.36% and 22.83% over existing LSTM- and EQNN-based prediction approaches, respectively.
Comments14 pages, 9 figures
Journal refIEEE Transactions on Systems, Man, and Cybernetics: Systems 2025
