Date:  Tuesday, Jan. 21st, 2020

Time:  10:00 am to 11:30 am

Location: 3105 Lambton Tower

 

Along with the acceptance of Service-Oriented Architecture (SOA) as a promising style of software design, the role that Quality of Service (QoS) plays in the success of SOA-based software systems has become much more significant than ever before. When QoS is documented as a Service-Level Agreement (SLA), it specifies the commitment between a service provider and a client, as well as monetary penalties in case of any SLA violations. To avoid and reduce the situations that may cause SLA violations, service providers need tools to intuitively analyze if their service design provokes SLA violations and to automatically guide them preventing SLA violations. Due to the dynamic nature of service interaction during the operation of SOA-based software systems, the avoidance of SLA violations requires prompt detection of potential violations before prevention takes place at real time. To overcome the low latency time in practice, this thesis research proposes an approach of using Machine Learning techniques to not only predict SLA violations but also prevent them by means of optimization. In addition to details of the proposed method, this presentation will also include a plan of experiments, which will help examining its usefulness for service providers working on the construction and refinement of services.

Internal Reader: Dr. Jianguo Lu

External Reader: Dr. Huapeng Wu

Advisor: Dr. Xiaobu Yuan