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Network management

About: Network management is a research topic. Over the lifetime, 17859 publications have been published within this topic receiving 234520 citations. The topic is also known as: computer network management & NM.


Papers
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Journal ArticleDOI
TL;DR: This paper applies a systematic classification of SDN faults, compares and analyze existing SDN fault management solutions in the literature, and conducts a gap analysis between solutions developed in an academic research context and practical deployments.
Abstract: Software-defined networking (SDN) has emerged as a new network paradigm that promises control/data plane separation and centralized network control While these features simplify network management and enable innovative networking, they give rise to persistent concerns about reliability The new paradigm suffers from the disadvantage that various network faults may consistently undermine the reliability of such a network, and such faults are often new and difficult to resolve with existing solutions To ensure SDN reliability, fault management , which is concerned with detecting, localizing, correcting and preventing faults, has become a key component in SDN networks Although many SDN fault management solutions have been proposed, we find that they often resolve SDN faults from an incomplete perspective which may result in side effects More critically, as the SDN paradigm evolves, additional fault types are being exposed Therefore, comprehensive reviews and constant improvements are required to remain on the leading edge of SDN fault management In this paper, we present the first comprehensive and systematic survey of SDN faults and related management solutions identified through advancements in both the research community and industry We apply a systematic classification of SDN faults, compare and analyze existing SDN fault management solutions in the literature, and conduct a gap analysis between solutions developed in an academic research context and practical deployments The current challenges and emerging trends are also noted as potential future research directions This paper aims to provide academic researchers and industrial engineers with a comprehensive survey with the hope of advancing SDN and inspiring new solutions

57 citations

Patent
20 Feb 2002
TL;DR: An intelligent communications capability that enhances legacy military tactical datalink systems by creating an interface between disparate civil and military communications systems onboard military aircraft, ground vehicles, and ground-based communications infrastructures is presented in this article.
Abstract: An intelligent communications capability that enhances legacy military tactical datalink systems by creating an interface between disparate civil and military communications systems onboard military aircraft, ground vehicles, and ground-based communications infrastructures. This capability performs various information management tasks to interface with avionics systems, ground vehicle computer systems, and ground-based infrastructure computer systems. The invention enhances message processing through automation in areas such as data collection, incoming message handling, outgoing message construction, communications network management, and message priority management and routing. The invention also incorporates learning techniques to improve the overall efficiency of integrated military missions, operations, and maintenance in areas such as intelligent consolidation of datalink information, intelligent message distribution, and adaptive message re-routing in response to communications network failures. These enhancements will benefit the military by improving mission effectiveness with real-time information integration and management while reducing their support costs.

57 citations

Journal ArticleDOI
TL;DR: In the paper, the reference architecture of SELFNET, which is divided into Infrastructure Layer, Virtualized Network Layer, SON Control Layer,SON Autonomic Layer, NFV Orchestration and Management Layer, and Access Layer, will be presented.
Abstract: To meet the challenging key performance indicators of the fifth generation (5G) system, the network infrastructure becomes more heterogeneous and complex. This will bring a high pressure on the reduction of OPEX and the improvement of the user experience. Hence, shifting today's manual and semi-automatic network management into an autonomic and intelligent framework will play a vital role in the upcoming 5G system. Based on the cutting-edge technologies, such as Software-Defined Networking and Network Function Virtualization, a novel management framework upon the software-defined and Virtualized Network is proposed by EU H2020 SELFNET project. In the paper, the reference architecture of SELFNET, which is divided into Infrastructure Layer, Virtualized Network Layer, SON Control Layer, SON Autonomic Layer, NFV Orchestration and Management Layer, and Access Layer, will be presented.

57 citations

Proceedings ArticleDOI
Runyuan Sun1, Bo Yang1, Lizhi Peng1, Zhenxiang Chen1, Lei Zhang1, Shan Jing1 
23 Sep 2010
TL;DR: Experimental results show that probabilistic neural network is an effective machine learning technique for traffic identification.
Abstract: Traffic classification, a branch of passive network measurement, becomes more and more important for network management. As traditional traffic classification techniques like port-based and payload-based techniques become ineffective for complicated internet applications which use dynamic port number and encryption techniques to avoid detection, machine learning based techniques gained more and more attentions in the past few years. But there are few studies that focus on applying neural computation techniques for traffic classification. In this paper, we use a distributed host based traffic collection platform (DHTCP) to gather traffic samples with accurate application information on user hosts. Then probabilistic neural network was used to traffic classification. Web and P2P traffics were studied since they are the most predominant internet traffic types. experimental results show that probabilistic neural network is an effective machine learning technique for traffic identification

57 citations

Journal ArticleDOI
TL;DR: Two novel anomaly detection mechanisms based on statistical procedure Principal Component Analysis and the Ant Colony Optimization metaheuristic are presented and compared, demonstrating that the systems are able to enhance the detection of anomalous behavior by maintaining a satisfactory false-alarm rate.

57 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202348
2022147
2021446
2020649
2019774
2018842