Welcome

The HDR-Nets workshop focuses on the application of the National Science Foundation’s (NSF) Harnessing the Data Revolution (HDR) Big Idea in the networking domain, particularly on the design, deployment, operation and management, and evolution of networking systems and services. This first-year workshop aims to bring together industrial practitioners and academic researchers to share ideas and visions on harnessing the latest data science and engineering technologies to solve the problems and challenges that network and service providers face today and in the near future. The workshop will feature technical sessions, invited speakers, and panel discussions on these topics. Participants will gain in-depth understanding of the challenges in creating next-generation networking technologies, in managing increasingly large and complex networking systems, and in delivering highly reliable and high-performing networked services that meet users’ ever-rising expectations. This workshop will open the dialogue and promote participants to form collaborations in creating innovative solutions to address those challenges, as well as validating and driving technology adoption in operational environment.

Program

TO BE DETERMINED

Call for Papers

Artificial Intelligence (AI) and Machine Learning (ML) technologies have achieved remarkable success nowadays in many application domains, e.g., natural language processing, voice recognition, and computer vision. Meanwhile, the ever increasing complexity and scale of today’s networks keep posing new challenges for network measurement and analysis techniques and tools. Advances in the CPU/GPU performance and progress in ML methods—particularly using neural networks—have made ML/AI capable of shedding light on the enormous amount of operational and systems data. Therefore, AI/ML has been effectively used in many critical networking data analytic functions, such as fault isolation, intrusion detection, event correlation, log analysis, capacity planning, and design optimization, just to name a few.

Moreover, networking has recently undergone a huge transformation enabled by new models resulting from softwarization, virtualization, and cloud computing. This has led to a number of novel architectures supported by emerging technologies such as Software-Defined Networking (SDN), Network Function Virtualization (NFV), edge computing, IoT, and 5G. On the other hand, maturing ML techniques, such as reinforcement learning and transfer learning, can potentially serve as basis for incorporating learning into automated network control. The emergence of enhanced design coupled with the increased complexity in networking systems and protocols has fueled the need for improved network autonomy in agile infrastructures, which can be combined with AI/ML techniques to execute efficient, rapid, trustworthy management operations. For example, the coupling of the programmable control of SDN with scientific innovations in AI/ML promises unprecedented opportunities for querying high-volume and high-velocity, distributed streaming data at scale. This new technical capability can provide the necessary information to the many different network monitoring and control tasks to enable efficient automation of autonomous networks .

The above directions can be seen to collectively fall into the National Science Foundations’ (NSF) Harnessing the Data Revolution (HDR) Big Idea, a national-scale activity to enable new modes of data-driven discovery that will allow new fundamental questions to be addressed at the frontiers of science and engineering, with the focus in computer and communication networks. In this workshop, we invite submissions of high-quality original technical and survey papers, which have not been published previously, on artificial intelligence and machine learning techniques and their applications to computer and communication networks, including but not limited to following topics:

Machine learning and data mining algorithms for networking
Data mining and big data analytics for network management
Reinforcement learning in network control and scheduling
Energy-efficient/green network operations using machine learning and data mining algorithms
Self-learning, machine learning and big data analytics for detecting network attacks and other security issues
Big data analytics and visualization for traffic analysis
Big data analytics/machine learning for network anomaly/outage/failure detection
Use case applications of harnessing networking data for business intelligence such as process optimization and vendor selection
Use case applications of harnessing networking data for enhanced service and user experience such as content recommendation, location-based service and advertising
Machine learning algorithms for fingerprinting network device/service
Natural language processing techniques for network log analytics
Machine learning algorithms and tools for network diagnostics and root cause analysis
Reinforcement learning and machine learning techniques in protocol design and optimization
Data driven network architectural and protocol design (e.g., for vehicle networks, IoT networks)
Learning algorithms for provisioning network resources
Autonomous networks in DCs, WANs, IXPs, wireless networks, cloud networks, CDNs, home networks, etc.
Machine learning, deep learning, reinforcement learning and other learning algorithms for IoT and 5G
Reinforcement learning and other learning techniques for virtualization techniques including NFV, SDN, SFC, etc.
Learning techniques for network slicing optimization
Open-source AI software for networking or networked applications
Techniques for anonymization and user privacy protection in networking data
Federated learning from distributed network data
Performance analysis (e.g., security, optimality, privacy) of ML algorithms as applied to networking problems

Important Dates

July 27, 2019 11:59pm Abstract deadline
August 2, 2019 11:59pm Paper submission deadline
August 23, 2019 Paper acceptance notification
August 31, 2019 Camera ready deadline
October 7, 2019 Workshop in Chicago in Conjunction with ICNP 2019

Origanizing Committees

Steering Co-Chairs
- K.K. Ramakrishnan (UC Riverside)
- Zhi-Li Zhang (Minnesota University)
General Co-Chairs
- Nick Duffield (Texas A&M University)
- Zihui Ge (AT&T Labs Research)
TPC Co-Chairs
- Dan Pei (Tsinghua University)
- Anwar Walid (Nokia Bell Labs)
Publicity Chair
- Shenglin Zhang (Nankai University)
Publication Chair
- Yongqian Sun (Nankai University)
Web Chair
- Kaixin Sui (BizSeer)

Technical Program Committee (More to join)

Theo Benson (Brown University)
Nick Duffield (Texas A&M University)
Zihui Ge (AT&T Labs Research)
Chuanxiong Guo (Bytedance)
Carlee Joe-Wong (Carnegie Mellon University)
Qi Li (Tsinghua University)
Xin Liu (UC Davis)
Ajay Mahimkar (AT&T Labs Research)
Dan Pei (Tsinghua University)
Kaixin Sui (BizSeer)
Yongqian Sun (Nankai University)
Anwar Walid (Nokia Bell Labs)
Jim (Jun) Xu (Georgia Tech)
Kuai Xu (Arizona State University)
Hui Zang (Futurewei Research)
Shenglin Zhang (Nankai University)
Ying Zhang (Facebook)
Jiaqi Zheng(Nanjing University, China)

Submission Instructions

Submissions must be original, unpublished work, and not under consideration at another conference or journal. Submitted papers must be at most six (6) pages long, including all figures, tables, references, and appendices in two-column 10pt IEEE format. Papers must include authors’ names and affiliations for single-blind peer reviewing by the PC. All accepted papers must be presented by one of the authors. Please submit your paper via https://hdr-nets19.hotcrp.com

Camera-Ready Instructions

When preparing the camera-ready version of the invited paper, please follow the following instructions:

Revise your manuscript to address the comments of the reviewers.
Generate a camera-ready version for your paper, which must be PDF-formatted, 6 pages including references, follow the IEEE formatting guidelines and verify the PDF is IEEE explore compatible (see the "Paper Formatting" and "Generating and Submitting IEEE Xplore-Compatible PDF" parts of detailed instructions on https://icnp19.cs.ucr.edu/camera.html).
Complete the IEEE Electronic Copyright Form (eCF) (see the "Electronic Copyright Form" part of detailed instructions on https://icnp19.cs.ucr.edu/camera.html).
The deadline of the camera-ready version is Saturday August 31th, 2019. The camera-ready version should be submitted by sending an email to [email protected] with the paper attached.
Every paper must be presented by an author at the conference. Please make sure that at least one of the authors registers for the workshop, to ensure publication of the paper. The early bird cut-off date for registration of accepted papers is September 3; please do not miss it. IF you attend only the workshop, then you DO NOT need to register for the main conference. (The registration website: https://icnp19.cs.ucr.edu/registration.html)