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Predicting host CPU utilization in the cloud using evolutionary neural networks

机译：使用进化神经网络预测云中的主机CPU利用率

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摘要

The Infrastructure as a Service (IaaS) platform in cloud computing provides resources as a service from a pool of compute, network, and storage resources. One of the major challenges facing cloud computing is to predict the usage of these resources in real time. By knowing future demands, cloud data centres can dynamically scale resources to decrease energy consumption while maintaining a high quality of service. However cloud resource consumption is ever changing, making it difficult for accurate predictions to be produced. This motivates the research presented in this paper which aims to predict in advance the level of CPU consumption of a host. This research implements evolutionary Neural Networks (NN), a powerful machine learning method, to make these predictions. A number of state of the art swarm and evolutionary optimization algorithms are implemented to train the neural networks to predict host utilization: Particle Swarm Optimization (PSO), Differential Evolution (DE) and Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES). The results of this research demonstrate that CMA-ES converges faster to a better solution on the training data. However when evaluated on the test data, DE performs statistically equal to CMA-ES. The results also demonstrate that the trained networks are still accurate when applied to CPU utilization data from different hosts with no further training needed. When evaluated to predict multiple steps into the future, the accuracy of the network understandably decreases but still performs well on average.
机译：云计算中的基础架构即服务（IaaS）平台通过计算，网络和存储资源池提供资源即服务。云计算面临的主要挑战之一是实时预测这些资源的使用情况。通过了解未来需求，云数据中心可以动态扩展资源以减少能耗，同时保持高质量的服务。但是，云资源消耗一直在变化，这使得难以进行准确的预测。这激发了本文中提出的旨在提前预测主机CPU消耗水平的研究。这项研究实现了进化神经网络（NN）（一种强大的机器学习方法）来做出这些预测。实现了许多最新的群体算法和进化优化算法，以训练神经网络预测主机利用率：粒子群优化（PSO），差分进化（DE）和协方差矩阵适应进化策略（CMA-ES）。这项研究的结果表明，CMA-ES可以更快地收敛到训练数据的更好解决方案。但是，在对测试数据进行评估时，DE在统计上等同于CMA-ES。结果还表明，将受过训练的网络应用于来自不同主机的CPU利用率数据时仍然是准确的，不需要进一步的训练。当进行评估以预测未来的多个步骤时，网络的准确性可以理解地下降，但平均而言仍然表现良好。

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