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Publication Date

First Quarter 2020

Manuscript Submission Deadline

Special Issue

Call for Papers

A key trend of current network evolution is towards network softwarization.  The softwarization/virtualization technology aims to enable a network to be programmable in a way that makes the network more flexible, scalable, and reliable, and in turn leads to agile service deployment, low capital and operational expenses, and having self-x properties.  Thus far, two widely adopted solutions have been Software Defined Networks (SDN) and Network Function Virtualization (NFV). Both SDN and NFV have become key enabling technologies for 5G networks and can be widely applicable to a range of important domains, including cloud datacenters, IoT, mobile edge computing (MEC), smart grid, cognition-based networks.

Although SDN and NFV facilitate the flexibility and scalability of network services and make the deployment of network services faster and cheaper, such software-based solution also introduces new problems, including throughput performance degradation and unstable jitter.  More specifically, in SDN/NFV-based networks, traffic engineering, resource management, and network security are among the challenges that telecommunication service providers need to overcome in order to provide better services to users and improve their revenue. Meanwhile, machine learning (ML) has seen great success in solving problems from various domains. It is believed that ML also has high potential in addressing the aforementioned challenges in SDN/NFV-based networks, especially in the elastic deployment of virtual network functions (VNFs), dynamic service provisioning, adaptive traffic control, and the security issues, as ML is a technology that can effectively extract the knowledge from data, and then accurately predict future resource requirements of each virtualized software-based appliance and future service demands of each user.  Though researchers and practitioners have started their research on exploring various ML techniques for leveraging the performance of these virtualized networks, a great deal of challenges are yet to be addressed. 

The aim of this special issue is thus to provide a forum for recent research results on the topics relevant to the technological challenges of leveraging ML technology in SDN/NFV-based networks.  We solicit high-quality original research works on various aspects of leveraging the performance of SDN/NFV-based networks with ML. Topics of interest include, but are not limited to:

  • Resource management in SDN/NFV-based networks with ML technology
  • Traffic engineering in SDN/NFV-based networks with ML technology
  • Elastic VNF placement and orchestration with ML technology
  • Energy efficiency in SDN/NFV-based networks with ML technology
  • VNF performance degradation and correction with ML technology
  • Security, Privacy, and Trust issues in SDN/NFV-based networks with ML technology
  • Novel and innovative machine learning methods for SDN/NFV-based autonomic networks

Submission Guidelines

Prospective authors should follow the IEEE J-SAC manuscript format described in the Information for Authors to submit their papers in pdf format to EDAS according to the following timeline. For additional information regarding this special issue, please contact David Wei.

Important Dates

Submission Deadline: 22 June 2019 (New Deadline)
First Notification: 31 August 2019
Acceptance Notification: 31 October 2019​​​​​​​
Final Manuscript Due: 15 November 2019
Expected Publication of the Special Issue​​​​​​​: First Quarter 2020

Guest Editors

David S. L. Wei
CIS Dept., Fordham University, USA

Kaiping Xue
Dept.  EEIS, University of Science and Technology of China, China

Roberto Bruschi
S3ITI Federated National Laboratory, CNIT, Italy

Akihiro Nakao
The University of Tokyo, Japan

Stefan Schmid
University of Vienna, Austria