The Second IEEE International Workshop on Harnessing the Data Revolution in Networking (HDR-Nets 2020)

Artificial Intelligence (AI) and Machine Learning (ML) technologies have achieved remarkable success nowadays in many application domains, e.g., natural language processing, biometrics, and computer vision. Meanwhile, the ever increasing complexity and scale of today’s networks keep posing new challenges for network measurement and analytics techniques and tools. Advances in the high-performance computing and progress in ML methods—particularly using deep learning—have made ML/AI capable of discovering valuable knowledge from enormous amounts 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 a 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, self-adaptive, rapid, and collaborating network systems.

HDR-Nets 2020 workshop is aligned with 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. More specifically, HDR-Nets 2020 workshop is targeting research at the intersection of machine learning and networking by bringing together experts from several research communities spanning communications and networking, machine learning, mobile computing, and big data. The agenda will includes discussions of significant contributions, community interests, new tools and research problems related to the design of intelligent, robust, and adaptive communications and networks with the aid of machine learning, as well as identifying best networking practices and design principles for learning systems.

Topics of Interest

We encourage interdisciplinary contributions of high-quality original technical and survey papers, which have not been published previously, bridging the gap between machine learning, communications, and networking from either a theoretical perspective or a practical point of view. Topics of interest include, but not limited to, the followings:

• Machine learning, deep learning, data mining and big data analytics algorithms for networking
• Reinforcement learning and machine learning techniques in network design, control, scheduling, and optimization
• Energy-efficient/green network operations using machine learning and data mining algorithms
• Machine learning for network data stream / time series analytics in real-time
• Algorithms for dealing with data-level difficulty in networking data, such as imbalance, noise, or high dimensionality
• Self-learning, machine learning and big data analytics for network intrusion/anomaly/outage/ failure detection
• Natural language processing techniques for network log analytics
• Data driven network architectural and protocol design (e.g., for vehicular networks, IoT networks)
• Learning algorithms for provisioning network resources
• Autonomous networks in DCs, WANs, IXPs, wireless networks, cloud networks, CDNs, home networks, etc.
• Reinforcement learning and other learning techniques for virtualization techniques including NFV, SDN, SFC, etc.
• Networking for efficient learning systems
• Machine learning at the network edge
• Federated learning and distributed networking
• New knowledge discovery theories and models from network systems data
• Data collection and visualization techniques for networks and applications
• Techniques for anonymization and user privacy protection in networking data
• Performance analysis (e.g., security, optimality, privacy) of ML algorithms as applied to networking problems
• Machine learning algorithms for fingerprinting network device/service
• Adversarial machine learning and networking
• Open-source AI software for networking or networked applications
• 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
• Human factors in ML and human-ML interactions for network systems

Workshop Organizers

Steering Co-Chairs

• Zhi-Li Zhang (University of Minnesota, USA)

General Co-Chairs

• Dan Pei (Tsinghua University, China)
• Anwar Walid (Nokia Bell Labs, USA)

TPC Co-Chairs

• Harshit Chitalia (Juniper Networks, USA)
• Bartosz Krawczyk (Virginia Commonwealth University, USA)
• Tamer Nadeem (Virginia Commonwealth University, USA)

Publicity Chair

• Hana B. Pasandi (Virginia Commonwealth University, USA)

Web Chair

• Santosh Nukavarapu (Virginia Commonwealth University, USA)

Important Dates

Paper submission deadline	July 17, 2020 11:59pm EDT extended to July 31, 2020 11:59pm EDT (FINAL)
Notification of acceptance	August 14, 2020
Camera ready copy due	August 24, 2020
Workshop date	October 13, 2020

Paper Submission Guidelines

All submissions must be original research not under review at any other venue. Submissions will be evaluated on the basis of technical quality, novelty, potential impact, and clarity. Solicited submissions include both full technical workshop papers and white position papers. Maximum length of such submissions is 6 pages in two-column 10pt IEEE Computer Society format, and if accepted they will be published by IEEE and appear in the IEEE Xplore. Formatting for all submissions (excluding page length) must adhere to the guidelines here. In accordance with the ICNP 2020 Conference, this workshop will adapt the double-blind review policy. All accepted papers must be presented by one of the authors.

Papers must be submitted electronically as PDF files via https://hdrnets20.hotcrp.com.

TPC Members

Zihui Ge (AT&T Labs Research, USA)
Maria Gorlatova (Duke University, USA)
Abhimanyu Gosain (Northeastern University, USA)
Chuanxiong Guo (Bytedance, China)
Carlee Joe-Wong (CMU, USA)
Xin Liu (University of California, Davis, USA)
Ajay Mahimkar (AT&T Labs Research, USA)
Gurkan Solmaz (NEC Laboratories Europe, Germany)
Yongqian Sun (Nankai University, China)
Isaac Triguero (Nottingham University, UK)
Mostafa Uddin (Nokia Bell Labs, USA)
Jun (Jim) Xu (Georgia Institute of Technology, USA)
Kuai Xu (Arizona State University, USA)
Shenglin Zhang (Nankai University, China)

Coronavirus update

Based on the feedback from the ICNP community, ICNP 2020 proceeds as a hybrid event where a majority of the attendees are expected to participate online only, and a minority are willing and able to attend in person. The in-person component of the conference will be conducted in Madrid according to the best infection-prevention practices. Please contact the General Chair if you have any questions.