Inheriting from a data-driven communication pattern other than a location-driven pattern, named data net- working （NDN） offers better support to network-layer dataflow. However, the application developers have to ha...Inheriting from a data-driven communication pattern other than a location-driven pattern, named data net- working （NDN） offers better support to network-layer dataflow. However, the application developers have to handle complex tasks, such as data segmentation, packet verification, and flow control, due to the lack of proper transport-layer protocols over the network layer. In this study, we design a dataflow-oriented programming interface to provide transport strategies for NDN, which greatly improves the efficiency in developing applications. This interface presents two application data unit; （ADU） retrieval strategies according to different data publishing patterns, in which it adopts an adaptive ADU pipelining algorithm to control the dataflow based on the current network status and data generation rate. The interface also offers network measurement strategies to monitor an abundance of critical metrics infuencing the application performance. We verify the functionality and performance of our interface by implementing a video streaming application spanning 11 time zones over the worldwide NDN testbed. Our experiments show that the interface can efficiently support developing high-performance and dataflow-driven NDN applications.展开更多
基金This work is supported by the National Natural Science Foundation of China under Grant No. 61373025.
文摘Inheriting from a data-driven communication pattern other than a location-driven pattern, named data net- working （NDN） offers better support to network-layer dataflow. However, the application developers have to handle complex tasks, such as data segmentation, packet verification, and flow control, due to the lack of proper transport-layer protocols over the network layer. In this study, we design a dataflow-oriented programming interface to provide transport strategies for NDN, which greatly improves the efficiency in developing applications. This interface presents two application data unit; （ADU） retrieval strategies according to different data publishing patterns, in which it adopts an adaptive ADU pipelining algorithm to control the dataflow based on the current network status and data generation rate. The interface also offers network measurement strategies to monitor an abundance of critical metrics infuencing the application performance. We verify the functionality and performance of our interface by implementing a video streaming application spanning 11 time zones over the worldwide NDN testbed. Our experiments show that the interface can efficiently support developing high-performance and dataflow-driven NDN applications.
