Personalized quality prediction for dynamic service management based on invocation patterns

Recent service management needs, e.g., in the cloud, require ser-vices to be managed dynamically. Services might need to be selected or re-placed at runtime. For services with similar functionality, one approach is to identify the most suitable services for a user based on an evaluation of the quality (QoS) of these services. In environments like the cloud, further person-alisation is also paramount. We propose a personalized QoS prediction method, which considers the impact of the network, server environment and user input. It analyses previous user behaviour and extracts invocation patterns from moni-tored QoS data through pattern mining to predict QoS based on invocation QoS patterns and user invocation features. Experimental results show that the pro-posed method can significantly improve the accuracy of the QoS prediction