IEEE Conference on Network Function Virtualization and Software Defined Networks
14–16 November 2022 // Chandler, AZ, USA


Keynote talk #1

Title: How Resilient is the Next Generation Networks: Security Challenges and Future Envisions

Speaker: Dr. Carol Fung

Organization: Concordia University


Dr. Carol Fung is an Associate Professor and Gina Cody Research Chair in Cybersecurity and the Internet of Things at Concordia University, Canada. She had her PhD degree from the University of Waterloo, Canada. Her research interests include Network Privacy and Security, the Internet of Things, Social Networks, and the Next Generation Network Management. She has more than 100 peer reviewed publications. She is an associate Editor for TNSM and COMNET.


In recent years, new network management technologies have emerged including SDN and NFV, which brought revolutionary impact to the network industry. In this talk, we will have a brief recap on recent development in new network technologies, and review the security challenges that we have encountered when developing the next generation networks. We will also envision security challenges that may emerge with new network technologies in the near future.

Keynote talk #2

Title: Designing the next generation Cloud Native Network Infrastructure

Speaker: Mr. Edwin Verplanke, Intel Senior Principal Engineer, Network Data Plane Architect

Organization: Intel


Edwin Verplanke is a passionate technologist focusing on hardware and software Data Plane acceleration technologies such as DPDK, VPP and more recently a newly designed Cloud Native Data Plane (CNDP). These acceleration technologies have been the corner stone for transitioning the communication industry from relying on fixed function communications solutions to using virtualized or containerized implementations.


The communications industry continues to evolve from Network Functions from virtual machine-based implementations to containerized cloud solutions. To support this transition Intel has been developing fully programmable hardware and software platforms integrating enhancements to meet the requirements of the cloud native networking functions. To meet the cloud native network microservice performance and scalability demands we explore the new capabilities of the developing capabilities such as Cloud Native Data Plane (CNDP) acceleration and the advantages of revolutionary SI features found in modern processor architectures that support the demands of data plane acceleration.

Keynote talk #3

Title: Data-Driven Network Management

Speaker: Dr. Kohei Shiomoto

Organization: Tokyo City University


Kohei Shiomoto is a Professor, Tokyo City University, Tokyo Japan. Since joining NTT Laboratories in 1989, he was engaged in research and development in the data communications industry on high-speed computer network architecture, traffic management, and network analysis to create innovative technologies for the Internet, mobile, and cloud computing. In 2017, he joined Tokyo City University to engage in research and education on data science and computer networking. Current research interests include data mining for network management, human flow analysis, cloud computing and blockchain. He has published more than 70 academic papers, 130 refereed international conference papers, and 6 RFCs in IETF. He served as the lead Series editor for the Network Softwarization and Management Series in IEEE Communications Magazine, 2018-2021. He has been serving as Associate Editor for IEEE Transactions on Network and Service Management.


Software-defined networking (SDN) and network function virtualization (NFV) are paradigms that enable a new system development. SDN separates the control plane from the forwarding plane of routers and switches, and NFV enables network functions previously realized on middlebox hardware on commodity servers. Although softwarization like SDN and NVF brings numerous benefits such as cost savings, capacity flexibility, and rapid deployment of new features, complexity increases. This complexity makes it difficult to apply traditional network management approaches that build analytical models of the entire system. Data-driven approaches are a new paradigm in network operations that use data mining techniques to enable correlation and causality inference, anomaly detection, root cause analysis, traffic prediction, and knowledge discovery. Data mining techniques, including machine learning, are key technologies in data-driven approaches. Machine learning does not require rules and instructions specified by computer programmers as in traditional computer science but instead learns algorithms by experience, just as humans do, to perform tasks such as inference, recognition, prediction, learning, and generation. This talk will review the related work on data mining applications to network management tasks and consider how data mining techniques, including machine learning, can solve network management challenges in the SDN-NFV era.


Keynote talk #4

Title: Navigating the Virtualization World Towards 6G: Agility, Reliability, Shannon and Beyond

Speaker: Dr. Martin Reisslein

Organization: School of Electrical, Computer, and Energy Engineering, Arizona State University


Martin Reisslein (S’96-M’98-SM’03-F’14) received the Ph.D. in systems engineering from the University of Pennsylvania, Philadelphia, PA, USA in 1998. He is currently a Professor with the School of Electrical, Computer, and Energy Engineering, and Program Chair of Computer Engineering at Arizona State University (ASU), Tempe, AZ, USA. He is currently an Associate Editor for IEEE Access, IEEE Transactions on Education, IEEE Transactions on Mobile Computing, and IEEE Transactions on Network and Service Management.  He currently serves as Area Editor for Optical Networking for the IEEE Communications Surveys and Tutorials and as Co-Editor-in-Chief of Optical Switching and Networking.  He served as Associate Editor of the IEEE/ACM Transaction on Networking (2009-2013), served as Associate Editor-in-Chief of the IEEE Communications Surveys and Tutorials (2007-2020), and chaired the Steering Committee of the IEEE Transactions on Multimedia (2017-2019). He received the IEEE Communications Society Best Tutorial Paper Award in 2008, a Friedrich Wilhelm Bessel Research Award from the Alexander von Humboldt Foundation in 2015, as well as a DRESDEN Senior Fellowship in 2016 and in 2019.


Agility: Mobile Edge Computing (MEC) brings the benefits of cloud computing, such as computation, networking, and storage resources, close to end users, thus reducing end-to-end latency and enabling various novel use cases.  However, frequent user mobility makes it challenging for the MEC to guarantee the close proximity to the users. To tackle this agility challenge, the underlying network has to be capable of seamlessly migrating applications between multiple MEC sites.  This application migration requires the quick and flexible migration of the application states without service interruption, while minimizing the state transfer cost.  We give an overview of the Flexible And low-latency State Transfer (FAST), the first programmable state forwarding framework. FAST flexibly and directly forwards states between source instance and destination instance based on Software-Defined Networking (SDN).

Reliability: Existing Network Function Virtualization (NFV) service placements that reuse existing network functions either reuse an entire Service Function Chain (SFC) or only individual network functions while ignoring the chain configuration cost for configuring the SFC traffic steering and ignoring the reliability of the network functions.  Also, the Mobile Edge Cloud (MEC) frameworks that are required to implement an NFV service placement should ideally seamlessly cooperate with the various existing NFV Management and Orchestration (MANO) frameworks. However, the existing MEC frameworks lack multi-MANO support.  We give an overview of the novel Subchain-Aware NFV service Placement (SAP) optimization model that accounts for the configuration cost for stitching together reused network functions to an SFC and strives to reuse existing subchains of consecutive network functions (with already deployed SFC traffic steering), while accounting for the recovery cost of network functions with limited reliability. Furthermore, we sketch the novel Automated Provisioning framework for MEC (APMEC) with open-source OpenStack implementation to enable the deployment of Tabu-SAP in real networks; APMEC supports multiple MANOs through a loose coupling MANO-MEC design.

Shannon and Beyond: Shannon communication transmits (conveys) a particular message via a channel, whereby the number of transmissible messages scales exponentially with the blocklength and code rate. In contrast, Identification via channels verifies whether a particular message at the destination is identical to a message at the source, whereby the number of identifiable messages scales double exponentially with the blocklength and code rate. Existing NFV agility and reliability mechanisms are exclusively based on Shannon communication. We pose the question whether and how NFV agility and reliability can benefit from Beyond Shannon paradigms in the semantic (goal-oriented) communication field, e.g., through verification of the synchronization of NFV states across distributed MEC nodes.

2022 Patrons

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