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

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

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

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Related Special Issue

Published Papers (3 papers)

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Research

8 pages, 1713 KB  
Communication
Design and Performance Evaluation of HEPS Data Center Network
by Shan Zeng, Tao Cui, Yanming Wang, Mengyao Qi and Fazhi Qi
Network 2025, 5(4), 53; https://doi.org/10.3390/network5040053 - 5 Dec 2025
Viewed by 234
Abstract
Among the 15 beamlines in the first phase of the High-Energy Photon Source (HEPS) in China, the maximum peak data generation volume can reach 1 PB per day, with the maximum peak data generation rate reaching 3.2 Tb/s. This poses significant challenges to [...] Read more.
Among the 15 beamlines in the first phase of the High-Energy Photon Source (HEPS) in China, the maximum peak data generation volume can reach 1 PB per day, with the maximum peak data generation rate reaching 3.2 Tb/s. This poses significant challenges to the underlying network system. To meet the storage, computing, and analysis needs of HEPS scientific data, this paper designed a high-performance and scalable network architecture based on RoCE (RDMA over Converged Ethernet). Test results demonstrate that the RoCE-based HEPS data center network system achieves high bandwidth and ultra-low latency, stably maintains reliable transmission performance during the interaction of scientific data storage, computing, and analysis, and exhibits excellent scalability to adapt to the future expansion of HEPS beamlines. Full article
(This article belongs to the Special Issue Advanced Technologies in Network and Service Management, 2nd Edition)
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22 pages, 537 KB  
Article
Efficient, Scalable, and Secure Network Monitoring Platform: Self-Contained Solution for Future SMEs
by Alfred Stephen Tonge, Babu Kaji Baniya and Deepak GC
Network 2025, 5(3), 36; https://doi.org/10.3390/network5030036 - 10 Sep 2025
Cited by 1 | Viewed by 1919
Abstract
In this paper, we introduce a novel, self-hosted Syslog collection platform designed specifically to address the challenges that small and medium enterprises (SMEs) face in implementing comprehensive syslog monitoring solutions. Our analysis begins with an assessment of current network observability practices, evaluating enterprise [...] Read more.
In this paper, we introduce a novel, self-hosted Syslog collection platform designed specifically to address the challenges that small and medium enterprises (SMEs) face in implementing comprehensive syslog monitoring solutions. Our analysis begins with an assessment of current network observability practices, evaluating enterprise solutions, on-premises systems, and Software as a Service (SaaS) offerings to identify features crucial for SME environments. The proposed platform represents an advancement in the field through the incorporation of modern practices, including GitOps and continuous integration and continuous delivery/deployment (CI/CD), and its implementation onto a self-managed Kubernetes platform, which is an approach not commonly explored in SME-focused solutions. We will explore its scalability by leveraging dynamic templates, which allow us to select the number and type of nodes when deploying networks of various sizes. This architecture ensures organisations can deploy a pre-designed, scalable network monitoring solution without extensive external support. The resilience of the proposed platform is assessed by providing empirical evidence of the scaling performance and reliability under various failure scenarios, including node failure and high network throughput stress. Full article
(This article belongs to the Special Issue Advanced Technologies in Network and Service Management, 2nd Edition)
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22 pages, 755 KB  
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
Cited by 3 | Viewed by 1802
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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