Improving the transmission control efficiency in Content Centric Networks

In this paper, we propose Flow-Aware Congestion Estimation (FACE) for congestion control in CCN. FACE predicts the future occupancy of the forwarding queue of the CCN router by historical flow information. Based on the current queue utilization, FACE estimates whether the congestion occurs more accurately. The Pending Interest Table (PIT), as a core component of the CCN router, is also crucial for the transmission control efficiency. Therefore, we then propose RTT-Aware PIT (RAPIT) for more precise transmission control. RAPIT measures the Round-Trip Time (RTT) between a CCN router and a content publisher, and sets PIT entry residence time dynamically based on the measured RTT. The evaluations show that FACE and RAPIT improve the PIT utilization and transmission control efficiency of CCN greatly.