Call for Papers

Thanks to the rapid growth in network bandwidth and connectivity, networks and distributed systems have become critical infrastructures that underpin much of today’s Internet services. However, networks are highly complex, dynamic and time-varying systems, such that the statistical properties of networks and network traffic cannot be easily modeled. Moreover, the trend towards highly integrated networks with diverse underlying access technologies to support simultaneously multiple vertical industries has demanded complex operations in network management. With the advent of Artificial Intelligence (AI) and Machine Learning (ML) techniques, along with the flexibility and programmability provided by the so-called softwarized networks and its enablers, Software-Defined Networks (SDN) and Network Function Virtualization (NFV), challenges associated to the complex network management operation of forthcoming networks can be tackled by exploiting the combination of AI/ML techniques and softwarized networks.
The main goal of IPSN Workshop is to present state-of-the-art research results and experience reports in the area of AI/ML for network management on softwarized networks, addressing topics such as artificial intelligence techniques and models for network and service management in softwarized networks; smart service orchestration, training process at the constrained edge, dynamic Service Function Chaining, Intent and policy based management, centralized vs distributed control of SDN/NFV based networks, analytics and big data approaches, knowledge creation and decision making. This workshop offers a timely venue for researchers and industry partners to present and discuss their latest results in the application of network intelligence to the management of softwarized networks. Topics of interest include, but are not limited to:

  • Data-driven management of software defined networks
  • Deep and Reinforcement learning for networking and communications
  • Experiences and best-practices using AI/ML in operational networks
  • Fault-tolerant network protocols exploiting AI/ML methods
  • Implications and challenges brought by computer networks to machine learning theory and algorithms
  • Innovative architectures and infrastructures for intelligent networks
  • Intelligent energy-aware/green softwarized networks
  • Intelligent in-network computing using softwarized networks
  • Intent & Policy-based management for intelligent networks
  • Methodologies for network problem diagnosis, anomaly detection and prediction
  • Network Security based on AI/ML techniques in softwarized networks
  • Open-source networking optimization tools for AI/ML applications
  • Protocol design and optimization using AI/ML in softwarized networks
  • Reliability, robustness and safety based on AI/ML techniques
  • Routing optimization based on flow prediction in softwarized networks
  • Self-learning and adaptive networking protocols and algorithms for softwarized networks
  • AI/ML for network management and orchestration in softwarized networks
  • AI/ML for network slicing optimization in softwarized networks
  • AI/ML for service placement and dynamic Service Function Chaining in softwarized networks
  • AI/ML for C-RAN resource management and medium access control
  • AI/ML for multimedia networking in softwarized networks
  • AI/ML support for ultra-low latency applications in softwarized networks

Submission instructions

Authors are invited to submit original contributions through Submissions Portal. Submitted manuscripts should use IEEE 2-column conference style and are limited to 6 pages (including references). Short/Work in progress papers are also welcome (4 page limit, including references). For more details, please check IPSN2024.

Important dates

  • Paper Submission Deadline: January 19, 2024 February 2, 2024 (Extended!!!)
  • Paper acceptance: March 1, 2024
  • Camera ready: March 15, 2024
  • Workshop Date: May 6 or May 10, 2024

Best papers will be invited to extend their work to a Journal Special Issue. To be confirmed.