In recent years we witnessed a growing interest towards leveraging Artificial Intelligence (AI) tools to innovate network operations at all layers, domains and planes. Yet, if, what and where we need to integrate intelligence in networks and how to (re)design networks for the native support of AI is still largely under debate. This is due to the multi-faceted nature of the challenges behind such integration: on the one hand, network architectures must be updated to accommodate AI models and their lifecycle by design (e.g., collecting and provisioning data in real-time, balancing centralized versus distributed computing approaches, empowering low latency requirements for fast closed-loop decision-making and network function automation); on the other hand, the design of AI models shall improve to better align with the myriad of requirements of production network systems (e.g., inference latency, computational complexity, trustworthiness of AI decisions); finally, operational procedures in research must be enhanced for verifiabilty, reproducibility and real-world deployment (e.g., establishing reference datasets, sharing trained models without sacrificing models explainability, robustness or safety).

The NativeNI – The 1st International Workshop on Native Network Intelligence, co-located with ACM CoNEXT 2022, held in Rome, Italy on December 9, 2022, will provide you with pragmatic answers to all these points are paramount to enable a transition of the current large body of literature on AI for networking from academic exercises to solutions integrated in production systems.

Please also find below a CfP to the first installment of NativeNI.

This workshop aims to bringing together researchers from academia and industry who are committed to making AI in networks a reality. We call for contributions from researchers working in the areas of network systems, applied machine learning and data science. We seek contributions that range from visionary position papers to promising ideas and prototypes, all the way to fully deployed solutions. All submissions should contribute to the common goal of making AI a viable and native technology for mobile networks.

Topics of interest include (but are not limited to):

• Network architectures and infrastructures for native AI support
• AI requirements for integration in network environments
• Network traffic data collection and analysis for AI support
• Low-latency AI for networks
• Compute-prudent AI for networks
• Tailored AI models for network management and orchestration
• Data availability for data-driven research and development
• Ethics in AI for networking
• On-device, cloud-driven or off-line application of AI for networking
• Centralized or distributed computational paradigm to support AI models
• AutoML and AI automation for networking
• Meta-learning for networking
• AI for Intent-Based Networking
• Explainability, robustness, safety of AI model deployments in networks
• Open-access datasets for the training and testing of AI models for networks
• Open-source tools for the assessment of AI models for networks
• Experimental deployments of AI in network systems.

Submission instructions

Authors should submit only original work that has not been published before and is not under submission to any other venue. All submitted papers will be assessed through a double-blind review process. This means that the authors do not see who are the reviewers and the reviewers do not see who are the authors.

Submissions should be six pages maximum, plus one page for references, in 2-column 10pt ACM format, and registered at https://nativeni22.hotcrp.com.

Please see the workshop website at https://nativeni.github.io/  for full information on the double submission process and paper formatting rules.

Important dates

• Abstract registration: September 23rd, 2022
• Submission:            September 30th, 2022
• Notification:          October 16th, 2022
• Camera ready:          October 25th, 2022
• Workshop Event:        December 9th, 2022

Contacts

For any questions please reach out to the workshop chairs:

• Alessandro Finamore (Huawei Technologies, France) – alessandro.finamore@huawei.com
• Marco Fiore (IMDEA Networks Institute) – marco.fiore@imdea.org
• Carlee Joe-Wong (Carnegie Mellon University) – cjoewong@andrew.cmu.edu