Now days the usage of cloud environment is rapidly increasing in all the fields to run applications in virtual machines instead of physical hardware based machine. This increases the service availability and also reduces the cost. The usage of openstack cloud environment is also increasing both in academics and industry as it provides open source cloud services to run the application both for research and for production environment. One of the challenges in cloud environment is that the detection and prediction of the anomalies before they occur. In the traditional approach, the anomalies are detected manually by keeping track of threshold level and heartbeat. The recent research is happening on using machine learning techniques in detecting the anomalies before they occur. In this paper, we propose a model for anomaly detection in openstack cloud environment. In the proposed model, we used Stacked and Bidirectional LSTM models to build the neural network. For the experiment the data is collected from openstack using collectd. The collected data sets 10 features and class label. Using LSTM neural network, we were able to detect the anomalies in openstack environment. The proposed model achieved the detection accuracy of 94.61% for training set and 93.98% for the test set using binary cross entropy function as a loss function.

如今，云环境的使用在所有领域中都在迅速增加，以在虚拟机（而不是基于物理硬件的机器）中运行应用程序。这增加了服务可用性并降低了成本。开放堆栈云环境的使用在学术界和行业中也在增加，因为它提供了开源云服务来在研究和生产环境中运行该应用程序。云环境中的挑战之一是在异常发生之前对其进行检测和预测。在传统方法中，通过跟踪阈值水平和心跳来手动检测异常。使用机器学习技术在异常发生之前进行检测的最新研究正在进行中。在本文中，我们提出了一种在开放式堆栈云环境中进行异常检测的模型。在提出的模型中，我们使用了堆叠和双向LSTM模型来构建神经网络。对于实验，使用收集的数据从openstack收集数据。收集的数据集有10个特征和类标签。使用LSTM神经网络，我们能够检测开放堆栈环境中的异常。提出的模型使用二进制交叉熵函数作为损失函数，对训练集和测试集的检测准确率分别为94.61％和93.98％。我们能够在开放堆栈环境中检测到异常。提出的模型使用二进制交叉熵函数作为损失函数，对训练集和测试集的检测准确率分别为94.61％和93.98％。我们能够在开放堆栈环境中检测到异常。提出的模型使用二进制交叉熵函数作为损失函数，对训练集和测试集的检测准确率分别为94.61％和93.98％。