Thesis abstract. The main goal of my research is to contribute to automated performance anomaly detection for large-scale and complex distributed systems, especially for Big Data applications within cloud computing. The main area that I am investigating are 1) an Automated detection of anomalous performance behaviors by finding the relevant performance metrics with which to characterize behavior of systems. 2) Performance anomaly localization: To pinpoint the cause of a performance anomaly due to internal or external faults. 3) Investigation of the possibility of anomaly prediction within the big data systems.
Short bio. I am currently a PhD student at the Department of Computing, Imperial College London. Previously, I was a senior academic researcher at National Centre for AI and Big Data Technologies KACST from 2012-2017. My research is on performance engineering for big data systems, with a specific focus on in-memory platforms. I am interested in big data systems, AI, IoT, HCP, complex distributed systems and cloud computing.