Title :
Intelligent Automated Diagnosis of Client Device Bottlenecks in Private Clouds
Author :
Widanapathirana, Chathuranga ; Li, J. ; Sekercioglu, Y.A. ; Ivanovich, Milosh ; Fitzpatrick, Paul
Author_Institution :
Dept. of Electr. & Comput. Syst. Eng., Monash Univ., Melbourne, VIC, Australia
Abstract :
We present an automated solution for rapid diagnosis of client device problems in private cloud environments: the Intelligent Automated Client Diagnostic (IACD) system. Clients are diagnosed with the aid of Transmission Control Protocol (TCP) packet traces, by (i) observation of anomalous artifacts occurring as a result of each fault and (ii) subsequent use of the inference capabilities of soft-margin Support Vector Machine (SVM) classifiers. The IACD system features a modular design and is extendible to new faults, with detection capability unaffected by the TCP variant used at the client. Experimental evaluation of the IACD system in a controlled environment demonstrated an overall diagnostic accuracy of 98%.
Keywords :
cloud computing; fault diagnosis; support vector machines; transport protocols; anomalous artifacts; client device bottlenecks; intelligent automated client diagnostic system; intelligent automated diagnosis; modular design; private cloud environments; support vector machine classifiers; transmission control protocol packet traces; Accuracy; Cloud computing; Computational fluid dynamics; Feature extraction; Servers; Training; Vectors; automated system; client diagnosis; cloud management; intelligent inference; packet traces;
Conference_Titel :
Utility and Cloud Computing (UCC), 2011 Fourth IEEE International Conference on
Conference_Location :
Victoria, NSW
Print_ISBN :
978-1-4577-2116-8
DOI :
10.1109/UCC.2011.42
