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.

Automatic configuration of the network devices via connection to specific switch ports

Abstract: A method and system for automatically configuring devices in a network is disclosed. A network management software application is provided that enables a user to associate policy settings with physical locations in the network. During an operational mode of the network management application, the application automatically detects when a network device is plugged into the network, and determines a location of the device in the network. The device is then automatically configured based on the policy settings associated with the corresponding location.

100 Gb/s optical time-division multiplexed networks

Abstract: We present ultrafast slotted optical time-division multiplexed networks as a viable means of implementing a highly capable next-generation all-optical packet-switched network. Such a network is capable of providing simple network management, the ability to support variable quality-of-service, self-routing of packets, scalability in the number of users, and the use of digital regeneration, buffering, and encryption. We review all-optical switch and Boolean logic gate implementations using an ultrafast nonlinear interferometers (UNIs) that are capable of stable, pattern-independent operation at speeds in excess of 100 Gb/s. We expand the capability provided by the UNI beyond switching and logic demonstrations to include system-level functions such as packet synchronization, address comparison, and rate conversion. We use these advanced all-optical signal processing capabilities to demonstrate a slotted OTDM multiaccess network testbed operating at 112.5 Gb/s line rates with inherent scalability in the number of users and system line rates. We also report on long-haul propagation of short optical pulses in fiber and all-optical 3R regeneration as a viable cost-effective means of extending the long-haul distance of our OTDM network to distances much greater than 100 km.

Internet Traffic Identification using Machine Learning

Abstract: We apply an unsupervised machine learning ap- proach for Internet traffic identification and compare the results with that of a previously applied supervised machine learning approach. Our unsupervised approach uses an Expectation Max- imization (EM) based clustering algorithm and the supervised approach uses the NaBayes classifier. We find the unsu- pervised clustering technique has an accuracy up to 91% and outperform the supervised technique by up to 9%. We also find that the unsupervised technique can be used to discover traffic from previously unknown applications and has the potential to become an excellent tool for exploring Internet traffic. I. INTRODUCTION Accurate classification of Internet traffic is important in many areas such as network design, network management, and network security. One key challenge in this area is to adapt to the dynamic nature of Internet traffic. Increasingly, new applications are being deployed on the Internet; some new applications such as peer-to-peer (P2P) file sharing and online gaming are becoming popular. With the evolution of Internet traffic, both in terms of number and type of applications, however, traditional classification techniques such as those based on well-known port numbers or packet payload analysis are either no longer effective for all types of network traffic or are otherwise unable to deploy because of privacy or security concerns for the data. A promising approach that has recently received some attention is traffic classification using machine learning tech- niques (1)-(4). These approaches assume that the applications typically send data in some sort of pattern; these patterns can be used as a means of identification which would allow the connections to be classified by traffic class. To find these patterns, flow statistics (such as mean packet size, flow length, and total number of packets) available using only TCP/IP headers are needed. This allows the classification technique to avoid the use of port numbers and packet payload information in the classification process. In this paper, we apply an unsupervised learning technique (EM clustering) for the Internet traffic classification problem and compare the results with that of a previously applied supervised machine learning approach. The unsupervised clus- tering approach uses an Expectation Maximization (EM) algo- rithm (5) that is different in that it classifies unlabeled training data into groups called "clusters" based on similarity. The NaBayes classifier has been previously shown to have high accuracy for Internet traffic classification (2). In parallel work, Zander et al. focus on using the EM clustering approach to build the classification model (4). We complement their work by using the EM clustering approach to build a classifier and show that this classifier outperforms the Na¨ Bayes classifier in terms of classification accuracy. We also analyze the time required to build the classification models for both approaches as a function of the size of the training data set. We also explore the clusters found by the EM approach and find that the majority of the connections are in a subset of the total clusters. The rest of this paper is organized as follows. Section II presents related work. In Section III, the background on the algorithms used in the Na¨ive Bayes and EM clustering approaches are covered. In Section IV, we introduce the data sets used in our work and present our experimental results. Section V discusses the advantages and disadvantages of the approaches. Section VI presents our conclusions and describes future work avenues.

Towards Autonomic Network Management: an Analysis of Current and Future Research Directions

Abstract: Autonomic network management is an innovative vision promising new horizons of efficient networking systems free from human control. This promise has, thus far, ushered in enormous yet dispersed research contributions in both industry and academia. The work presented in this article aims at putting these efforts into perspective deriving a more holistic view of the literature in this area. We analyze the requirements and the main contributions for the building blocks of any autonomic network management system (ANMS). We then describe a coherent classification methodology to compare existing ANMS architectures. Based on this analysis, we suggest a reference framework and highlight some open challenges and describe new research opportunities.

System and method for synchronizing the configuration of distributed network management applications

Abstract: A change management system to synchronize the configuration of network management applications. Traditional network management systems are maintained by hand-entering device lists into individual network management applications with no common-ties between the different applications. Whenever a network management application is changed or upgraded, it frequently becomes necessary to insure that the upgrade is populated throughout the network in order for devices to talk to one another in an error free way. The present invention is a system and method that automates the change management process in a real-time using a two-way communications model that permits a central database to affect changes on all or some network management applications/systems in the field, while also allowing those same field systems to affect the central database thereby reducing the time required for updating and monitoring a system when device changes take place.