Realistic data transfer scheduling with uncertainty

Next Generation Networks (NGNs) will be comprised of different access technologies. We are already seeing the emergence of mobile devices with the capability of connecting to heterogeneous networks with different capabilities and constraints. In addition, many bandwidth intensive applications have rather relaxed real-time constraints allowing for alternative scheduling mechanisms which can take into account user preferences, network characteristics as well as future network resource availability to better exploit network heterogeneity. The current approaches either simply react to changes, or assume that availability predictions are perfect. In this paper, we propose a scheduling scheme based on stochastic modeling to account for prediction errors. The scheme optimizes overall user utility gain considering imperfect predictions taken over realistic time intervals while catering for different applications' needs. We use 180 days of real user data of many users to demonstrate that it consistently outperforms other non-stochastic and greedy approaches in typical networking environments.