Do researchers have the data to take networking to the next level?

Computer networks and communications systems are increasingly complex, but traditional model-based approaches are still used to manage networks that now span mobile, edge and cloud computing, distributed systems and network stacks along with both wired and wireless systems.

To develop the next generation of high-performance networks, researchers have been relying on machine learning and other data intensive techniques – both of which may require large representative datasets for training and testing the viability of their algorithms and protocols.

In an April 14 Federal Register notice, the National Science Foundation asked the research community what kinds of datasets they need to spur advances in computer and network systems. The request for information seeks input related to data collecting and sharing and leveraging public or private datasets. For example, the NSF’s for Advanced Wireless Research program, which has established urban testbeds for new communication and networking technologies, may offer opportunities for collecting data researchers need, the science agency said.

NSF expects researchers to identify requirements for datasets that may include spectrum data, physical layer data, network and internet measurement data, workload data, power/performance data, and other systems data. While some networking and communications datasets are currently available, NSF said it is particularly interested in needs that are not currently met by these existing datasets, conventions or formats that may broaden the usability of the data and ways in which additional high-quality datasets may be made available to the research community.

NSF is inviting researchers to fill out a survey describing their research, the kind of datasets that would further their efforts, whether they have access to quality datasets and what formats and metadata they need. It also wants to hear from researchers who can contribute datasets to other scientists and what concerns they might have over privacy or the impact of anonymization on data quality.

By identifying areas where the lack of quality datasets may be holding back research, NSF aims to take research to the next level, and spur innovations in advanced wireless and artificial intelligence, which have been identified as strategic priority areas.

NSF anticipates making submissions publicly accessible through a website and will use the information to inform and refine future investments.

Submissions are due May 21.

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