SIGCOMM Computer Communication Review '21 | Data-driven Networking Research: models for academic collaboration with industry (a Google point of view)

[mental2008]

Presented in ACM SIGCOMM Computer Communication Review '07: https://dl.acm.org/doi/10.1145/3503954.3503960

Authors: Jeffrey C. Mogul, Priya Mahadevan, Christophe Diot, John Wilkes, Phillipa Gill, Amin Vahdat Google

[mental2008]

The biggest help the academia can get from industry is their scenarios and real-world, large scale data.

Google explains several constraints when sharing data with the academia:

• Hard constraints
  1. Privacy (even the interns and visiting scientists never have access to user data)
  2. Business concerns (even anonymize the data still have risks to expose technical information or business-growth information)
• Soft constraints
  1. Scale (the collection of large-scale infrastructural data is expensive to build and operate)
  2. Operational Risk (even seemingly-safe changes may cause problems)
  3. Staff time (releasing high-quality and datasets will cost much time)

All in all, they think working temporarily as employees such as interns and visiting scientists will be the best option. While here they focus on the field of network research, I believe it is a common problem if we're going to do some collaborations between the academia and industry.

Maybe if we want to do some real-world research in academia, just work as a temporal employee with little money and directly handle these dirty data in the industry?