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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.

Background Overlap syndrome (OS) is a challenging condition with an associated increased mortality. Noninvasive positive pressure ventilation is the main therapy in OS. Tri-level positive airway pressure (Tri-PAP) therapy is a new method of ventilatory support that was proposed to improve breathing during the cyclic breathing phases and correct the hypercapnia. Objectives To assess different noninvasive modalities of positive airway pressure including continuous positive airway pressure (CPAP), bi-level positive airway pressure (Bi-PAP), and Tri-PAP in the management of OS. Patients and methods This prospective interventional clinical trial was conducted on 45 patients with OS. After confirming the diagnosis of OS, patients were classified into three comparable groups, and in each group, we used one of the following three modalities of noninvasive ventilation: Tri-PAP, Bi-PAP, and CPAP. Arterial blood gas (ABG) was done after the use of the PAP. Data of sleep reports were recorded on each night and then analyzed. Results Patients were divided into three equal numbers and comparable groups in whom CPAP, Bi-PAP, and Tri-PAP were applied, correspondingly. Statistically significant improvement was observed regarding sleep parameters (apnea/hypopnea index, oxygen desaturation index, and snoring index) and ABG parameters [pH, partial pressure of carbon dioxide in arterial blood (mmHg), partial pressure of oxygen in arterial blood (mmHg), and arterial oxyhemoglobin saturation (%)] when Tri-PAP was compared with both Bi-PAP and CPAP.