Anomaly Detection and Bottleneck Identification of The Distributed Application in Cloud Data Center using Software–Defined Networking

Egyptian Informatics Journal

Nawal A. El-Fishawy¹  Ahmed M. El-Shamy²  Mokhtar A. A. Mohamed³  Gamal Attiya³ 

[1] Corresponding author.;Business Technology Department, Canadian International College CIC, Cairo, Egypt;Computer Science and Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt;

关键词: Cloud data center network;    Software-defined networking;    Anomaly detection;    Bottleneck identification;    Machine learning;    Distributed application;   

DOI  :  

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown