一个多重控制的托福利驱动的自适应量子神经网络模型,用于云环境中的动态工作负载预测
概括
本研究介绍了一种新的多重控制托福驱动的自适应量子神经网络 (MCT-AQNN),用于云计算工作负载预测. MCT-AQNN模型显著提高了预测动态和波动性云工作负载的准确性.
科学领域:
- 云计算 云计算 云计算 云计算
- 人工智能的人工智能
- 量子计算是一种量子计算.
背景情况:
- 云计算在动态资源扩展,负载平衡和功耗方面面临着挑战.
- 准确的工作负载预测对于解决这些云计算挑战至关重要.
- 现有的工作负载预测方法与动态云工作负载的高变异性作斗争.
研究的目的:
- 为准确的云工作负载预测引入一种新型模型.
- 为了解决当前处理挥发性云工作负载的方法的局限性.
- 通过量子学习优化探索,适应和利用能力.
主要方法:
- 介绍了一种新的多重控制托福利驱动的自适应量子神经网络 (MCT-AQNN) 模型.
- 量子计算的适应性与机器学习算法相结合.
- 在量子神经网络 (QNN) 的隐藏和输出层中使用多重控制托福利 (MCT) 门.
- 为训练QNN而开发了一种统一自适应量子机器学习 (UAQL) 算法.
主要成果:
- MCT-AQNN模型在工作负载预测方面表现出卓越的性能.
- 在四个现实世界的基准数据集上进行的实验显示了显著的准确性改进.
- 与最先进的方法相比,拟议的模型达到高达32%-96%的准确性.
结论:
- MCT-AQNN模型为复杂和弹性的云工作负载预测提供了有效的解决方案.
- 量子增强的机器学习从动态工作负载提供了更精确的相关性.
- 这种新的方法提高了云环境中的学习能力和预测准确性.
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