Hutchins, Ron

Nikolich, Anita

Wang, Kuang-Ching

English

Clemson University: TigerPrints

Workshop on Data for AI in Network Systemshttps://renci.github.io/ai-for-networking-workshop/[10/5/2023 10:33:38 AM]Workshop on Data for AI in Network SystemsDateOctober 20-21, 2021LocationVirtual, via ZoomExecutive SummaryNetwork systems have long relied on data — traffic measurements, system configurations, demand predictions, etc. — forplanning, engineering, operations, and trouble-shooting purposes. In the era of artificial intelligence (AI), data will be evenmore directly driving the network systems where AI methods are employed. A plethora of recent programs at NSF andacross federal agencies — e.g., National Research AI Institutes and a rich slate of other programs — will be ushering inexciting innovative ideas to apply AI in network systems. Enabling this exciting wave of research demands extraordinary newnetwork data, as well as methods and policies for collecting them. AI researchers will be exploring new kinds of data, atunprecedented scopes, granularities, and scales, to revolutionize how network systems support the broad range ofnetworked applications our society depends on. New application ideas would likely need new data, whereas new kinds ofdata would reveal new insights about the networks, applications, and their users, and would stimulate even more newapplication ideas. In a future world where AI is pervasively deployed across networks and applications the society dependson, having a good understanding of the data that will be shaping them and subsequently shaping us is timely and critical.What data will be made available for AI network systems? How will they be collected and used? What properties of suchdata, and in what ways, would they impact the networks and applications in technical, legal, and ethical contexts? And, mostimportantly, how will the mindsets of AI researchers, developers, and our next generation of students evolve and transformas we head into the future?The Workshop on Data for AI in Network Systems intends to facilitate a discussion among AI, networking, and securityresearch communities on:Event SummaryRegistrationCall for WhitepapersAgendaWorkshop on Data for AI in Network Systemshttps://renci.github.io/ai-for-networking-workshop/[10/5/2023 10:33:38 AM]RENCI © 2021What AI applications can be envisioned for network systems;What data are relevant to AI in network systems;How will data influence network systems and the society;How to enable scalable and responsible data collection for AI in network systems.Techniques to share data setsWhile discussion of these questions will most certainly be in the contexts of various forms of AI methods and algorithms, theparticipants are encouraged to focus more on the logical relation among data, system, and application objectives than onalgorithm specifics. Participants are also encouraged to broaden the scope of consideration beyond network systems toinclude the applications, systems (e.g., edge, cloud) and people, and discuss how AI solutions’ direct and indirect influenceson the stakeholders in the society in technical, legal, and ethical contexts.BackgroundAI is used more successfully today for network security functions such as malware identification and mail filtering.Development of tools for these particular use cases has been accelerated because researchers have been able to acquireopenly-shard large data sets to construct and train their endless ideas. For example, the publicly available EMBER (ElasticMalware Benchmark for Empowering Researchers) and SoReL-20M data sets contain millions of malware signatures withraw features.The use of AI and ML on other network functions is far less. Aside from attempts to “AI-enable” certain legacy networkfunctions, the “closed” nature of today’s network systems meant that very few researchers have the visibility into them tohave sufficient information and motivation to explore new possibilities. The data available to AI researchers today are verylimited, such as 1) bandwidth, 2) latency, 3) n-tuple with source, destination, and protocol, etc. This characterization ofnetworks is not sufficient for use by applications in the future. To stimulate new AI ideas for network systems, it is importantto consider objectives beyond the raw performance and economics of the infrastructure alone. Societal equity and ethicalconsiderations are as important for networks AI research. Increasingly, errors and biases witnessed in AI applications can betraced back to their training data not grounded in equity, fairness, accountability, transparency and ethics (FATE).OrganizersThis workshop is funded by the NSF and organized by Anita Nikolich, Ron Hutchins, Kuang-Ching Wang, and Tho Nguyen.

Workshop on Data for AI in Network Systems - Event Summary

https://tigerprints.clemson.edu/context/dains/article/1000/viewcontent/Workshop_on_Data_for_AI_in_Network_Systems.pdf

Workshop on Data for AI in Network Systems - Event Summary

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