Service assurance is critical for high-demand services running on telecom clouds. While service performance metrics may not always be available in real time to telecom operators or service providers, service performance prediction becomes an important building block for such a system. However, it is generally hard to achieve. In this master thesis, we propose a machine-learning based method that enables performance prediction for services running in virtualized environments with Docker containers. This method is service agnostic and the prediction models built by this method use only device statistics collected from the server machine and from the containers hosted on it to predict the values of the service-level metrics experienced on the client side. The evaluation results from the testbed, which runs a Video-on-Demand service using containerized servers, show that such a method can accurately predict different service-level metrics under various scenarios and, by applying suitable preprocessing techniques, the performance of the prediction models can be further improved. In this thesis, we also show the design of a proof-of-concept of a Real-Time Analytics Engine that uses online learning methods to predict the service-level metrics in real time in a container-based environment.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:kth-175889 |
| Date | January 2015 |
| Creators | Jiang, Zuoying |
| Publisher | KTH, Kommunikationsnät |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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