Source Selection in Transfer Learning for Improved Service Performance Predictions

Learning performance models for network and cloud services is challenging due to the dynamics of the operational environment stemming from network changes, and scaling and migration decisions in the cloud. This requires exchange or adaptation of the models in order to maintain prediction accuracy over time. Approaches that incorporate previously acquired knowledge using transfer learning is a viable technique for timely and robust model adaptation, especially when the training data is limited. In this paper, we study the challenge of source selection in transfer learning for improved service performance prediction. We quantify the impact of different source domains on the accuracy of a target model in another domain. The evaluation is performed using data traces obtained from a testbed that runs a Video-on-Demand service and a Key-Value Store under various load conditions. We find that the choice of source domain can yield a transfer gain, and sometimes a substantial transfer penalty. To mitigate this, we propose and evaluate two source-selection approaches with the aim of selecting a source domain with relevant knowledge for the target domain. A key result is that such source selection should encourage source-domain diversity rather than domain similarity in scenarios with few samples in the target domain.