Cloud computing applications have grown rapidly in the last decade, where many organizations are migrating their applications to cloud data center as they expected high performance, reliability, and the best quality of service. Data centers deploy a variety of technologies, such as software-defined networks (SDN), to effectively manage their resources. The SDN approach is a highly flexible network architecture that automates network configuration using a centralized controller to overcome traditional network limitations. This paper proposes an SDN-based monitoring algorithm to detect the performance anomaly and identify the bottleneck of the distributed application in the cloud data center using the support vector machine algorithm. It collects the data from the network devices and calculates the performance metrics for the distributed application components that are used to train the SVM algorithm and build a baseline model of the normal behavior of the distributed application. The SVM model detects performance anomaly behavior and identifies the root cause of bottlenecks using one-class support vector machine (OCSVM) and multi-class support vector machine (MCSVM) algorithms. The proposed method does not require any knowledge about the running applications or depends on static threshold values for performance measurements. Simulation results show that the proposed method can detect and locate the failure occurrences efficiently with high precision and low overhead compared to statistical methods, Naive Bayes Classifier and the decision tree machine learning method.

在过去十年中，云计算应用程序发展迅速，许多组织正在将其应用程序迁移到云数据中心，因为他们期望获得高性能、可靠性和最佳服务质量。数据中心部署了多种技术，例如软件定义网络 (SDN)，以有效管理其资源。SDN 方法是一种高度灵活的网络架构，它使用集中控制器来自动化网络配置，以克服传统网络的局限性。本文提出了一种基于SDN的监控算法，利用支持向量机算法检测云数据中心的性能异常并识别分布式应用的瓶颈。它从网络设备收集数据并计算分布式应用程序组件的性能指标，用于训练 SVM 算法并构建分布式应用程序正常行为的基线模型。SVM 模型使用一类支持向量机 (OCSVM) 和多类支持向量机 (MCSVM) 算法检测性能异常行为并确定瓶颈的根本原因。所提出的方法不需要有关正在运行的应用程序的任何知识，也不需要依赖于性能测量的静态阈值。仿真结果表明，与统计方法、朴素贝叶斯分类器和决策树机器学习方法相比，所提出的方法能够以高精度和低开销有效地检测和定位故障发生。