Transfer learning has been proposed as an approach for leveraging already learned knowledge in a new environment, especially when the amount of training data is limited. However, due to the dynamic nature of future networks and cloud infrastructures, a new environment may differ from the one the model is trained and transferred from. In this paper, we propose and evaluate an approach based on neural networks for heterogeneous transfer learning that addresses model transfer between environments with different input feature sets, which is a natural consequence of network and cloud re-orchestration. We quantify the transfer gain, and empirically show positive gain in a majority of cases. Further, we study the impact of neural-network architectures on the transfer gain, providing tradeoff insights for multiple cases. The evaluation of the approach is performed using data traces collected from a testbed that runs a Video-on-Demand service and a Key-Value Store under various load conditions.