Advanced Technologies in Network and Service Management, 2nd Edition

Special Issue Editors


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Guest Editor
Computer Science & Software Engineering Department, Gina Cody School of Engineering, Concordia University, Montreal, QC, Canada
Interests: M2M communication; wireless networks; optical networks; machine learning
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Electrical Engineering Department, University at Buffalo, Buffalo, NY 14260, USA
Interests: Internet of Things; wireless communications; cellular networks (4G/5G); network performance analysis and simulation; communications for the smart grid; optimization; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Networks and their provided services keep increasing in speed, size, and complexity, creating ongoing challenges to their management and control requirements. As a result, emerging tools and advanced technologies are being solicited to provide key solutions for reliable and effective network and service management.

Due to the diversity of networks and services, many tools and technologies, ranging from enabling infrastructures to innovative protocols and efficient algorithms, are necessary for scalable and streamlined management. Furthermore, a reliable and cost-effective management solution combines different technologies and exploits new network and service management venues. Such a solution is the target for infrastructure and service providers as well as the research community.

This Special Issue invites researchers to contribute original papers in areas including (but not limited to) the following:

  • Machine learning and artificial intelligence for network and service management;
  • Innovative architectures and protocols for network and service management;
  • Innovative technologies for network and service management;
  • Cloud/fog/edge computing;
  • Prototype implementation and testbed experimentation;
  • Software-defined networks (SDN);
  • Network function virtualization (NFV);
  • Network orchestration;
  • Network monitoring and measurements;
  • Data mining and (big) data analysis;
  • Fault management;
  • Network security;
  • Management based on the quality of experience;
  • Energy-aware management;
  • Simulations for network and service management;
  • Analytical model for network and service management;
  • Network optimization.

Dr. Hakim Mellah
Dr. Filippo Malandra
Guest Editors

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Keywords

  • network management
  • service management
  • software-defined networks
  • network optimization
  • network orchestration
  • network function virtualization

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Related Special Issue

Published Papers (1 paper)

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Research

22 pages, 755 KiB  
Article
Traffic-Driven Controller-Load-Balancing over Multi-Controller Software-Defined Networking Environment
by Binod Sapkota, Babu R. Dawadi, Shashidhar R. Joshi and Gopal Karn
Network 2024, 4(4), 523-544; https://doi.org/10.3390/network4040026 - 15 Nov 2024
Viewed by 523
Abstract
Currently, more studies are focusing on traffic classification in software-defined networks (SDNs). Accurate classification and selecting the appropriate controller have benefited from the application of machine learning (ML) in practice. In this research, we study different classification models to see which one best [...] Read more.
Currently, more studies are focusing on traffic classification in software-defined networks (SDNs). Accurate classification and selecting the appropriate controller have benefited from the application of machine learning (ML) in practice. In this research, we study different classification models to see which one best classifies the generated dataset and goes on to be implemented for real-time classification. In our case, the classification and regression tree (CART) classifier produces the best classification results for the generated dataset, and logistic regression is also considerable. Based on the evaluation of various algorithmic outputs for the training and validation datasets, and also when execution time is taken into account, the CART is found to be the best algorithm. While testing the impact of load balancing in a multi-controller SDN environment, in different load case scenarios, we observe network performance parameters like bit rate, packet rate, and jitter. Here, the use of traffic classification-based load balancing improves the bit rate as well as the packet rate of traffic flow on a network and thus considerably enhances throughput. Finally, the reduction in jitter while increasing the controllers confirms the improvement in QoS in a balanced multi-controller SDN environment. Full article
(This article belongs to the Special Issue Advanced Technologies in Network and Service Management, 2nd Edition)
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