Data-Driven Networking: Harnessing the “Unreasonable Effectiveness of Data” in Network Design

نویسندگان

  • Junchen Jiang
  • Vyas Sekar
  • Ion Stoica
  • Hui Zhang
چکیده

The last few years have witnessed the coming of age of data-driven paradigm in various aspects of computing (partly) empowered by advances in distributed system research (cloud computing, MapReduce, etc). In this paper, we observe that the benefits can flow the opposite direction: the design of distributed systems can be improved by data-driven paradigm. To this end, we present DDN, a new design framework for network protocols based on data-driven paradigm. We argue that DDN has the potential to significantly achieve better performance through harnessing more data than one single flow. Furthermore, we systematize existing instantiations of DDN by creating a unified framework for DDN, and use the framework to shed light on the common challenges and reusable design principles. We believe that by systematizing this paradigm as a broader community, we can unleash the unharnessed potential of DDN.

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تاریخ انتشار 2016