›› 2018, Vol. 33 ›› Issue (1): 158-168.doi: 10.1007/s11390-018-1812-9

Special Issue: Computer Architecture and Systems; Computer Networks and Distributed Computing

• Computer Architecture and Systems • Previous Articles     Next Articles

A Dataflow-Oriented Programming Interface for Named Data Networking

Li-Jing Wang1, Student Member, IEEE, Yong-Qiang Lv2, Member, CCF, ACM, IEEE, Ilya Moiseenko3, Dong-Sheng Wang1,2,*, Senior Member, CCF, ACM, IEEE   

  1. 1 Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China;
    2 Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing 100084, China;
    3 Computer Science Department, University of California, Los Angeles, CA 90095, U.S.A
  • Received:2016-08-20 Revised:2017-04-02 Online:2018-01-05 Published:2018-01-05
  • Contact: Dong-Sheng Wang E-mail:wds@tsinghua.edu.cn
  • About author:Li-Jing Wang received her Ph.D. degree from Tsinghua University, Beijing, in 2017. She is now a software engineer at Sogou, Inc. She was a visiting student at UCLA (University of California, Los Angeles) from 2014 to 2015. Her research interest focuses on distributed system.
  • Supported by:

    This work is supported by the National Natural Science Foundation of China under Grant No. 61373025.

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Inheriting from a data-driven communication pattern other than a location-driven pattern, named data networking (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 influencing 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.

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