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.

Systems and methods for providing global server load balancing of heterogenous devices

Abstract: The present invention provides improvements to load balancing by providing a load balancing solution that distributes a load among a plurality of heterogenous devices, such as different types of local load balancers, using metrics collected from the different devices. The load balancing appliance collects metrics from heterogenous devices using a network management protocol and communication model, such as a Simple Network Management Protocol (SNMP). These heterogenous device metrics are available on the load balancing appliance with appliance determined metrics and metrics obtained by the appliance from homogenous devices using a metric exchange protocol. Via a configuration interface of the appliance, a user can select one or more of these different metrics for global load balancing. As such, the load balancing appliance described herein obtains a multitude of metrics from the different devices under management. Additionally, the load balancing appliance described herein provides great flexibility in allowing the user to configure the global load balancer based on the user's understanding of these multitudes of metrics and to take into account the different characteristics and behaviors of the heterogenous devices.

OSI systems and network management

Abstract: Data communications standards to allow exchange of information between two application processes in different heterogeneous computing environments have been developed by International Standards groups. With the development of these standards, the need for managing the communications protocols was realized as part of both the Internet and OSI standards suites. This article addresses the network management paradigm developed as part of OSI standards. The OSI network management application includes three different aspects: categories of network management, a protocol that specifies the structure for transferring network management information, and information models that define resource-specific management information for the specific management functions. These three aspects will be described in this article. Network management functions are grouped into five categories: configuration, fault, performance, security, and accounting. The resource is managed to accomplish these functions. These five categories have been used not only in OSI network management but also in specifying the management functions for telecommunications network. These five categories are briefly discussed in the paper. The protocol structure for OSI network management is defined as an application service element known as CMISE. Regardless of the resource being managed, the protocol defines a basic set of operations applicable to network management. The article discusses the semantics of the various operations and the parameters associated with each operation. Using the structure defined by the protocol, for the various management functions, information is modeled to represent the managed resource. Object-oriented principles are used in defining information models. An introduction to these principles is provided. The management information exchanged is a combination of the three aspects. As part of OSI network management, information models to represent communication entities have been developed. An example is shown to illustrate the exchanged message for a management function. The article reiterates the three aspects and points out the advantages offered by this network management paradigm.

Method and apparatus for selecting network entities

Abstract: Network management can be made more efficient by using a graphical user interface that is clear and concise in what network information is identified and subsequently displayed. One key to improving graphical user interfaces is the categorizing of network entities into a series of attribute layers that define different features of the network entities. For instance, a layer of attributes may include geographical regions, services, customers, or types of network entities. By selecting a base view from the group of geographical regions and then network features for filtering operations from one or more of the other attribute layers, the network entities defined by the selected network features can be identified and isolated with all other irrelevant network entities being filtered out. The advantages to this graphical user interface is especially evident when considering the case that a specific customer, service, or network entity requires special attention. Other features of this interface include bookmarking, archiving, and monitoring specific views of a network based off a base view along with one or more filtered network features.

Adaptive Sampling for Network Management

Abstract: High-performance networks require sophisticated management systems to identify sources of bottlenecks and detect faults. At the same time, the impact of network queries on the latency and bandwidth available to the applications must be minimized. Adaptive techniques can be used to control and reduce the rate of sampling of network information, reducing the amount of processed data and lessening the overhead on the network. Two adaptive sampling methods are proposed in this paper based on linear prediction and fuzzy logic. The performance of these techniques is compared with conventional sampling methods by conducting simulative experiments using Internet and videoconference traffic patterns. The adaptive techniques are significantly more flexible in their ability to dynamically adjust with fluctuations in network behavior, and in some cases they are able to reduce the sample count by as much as a factor of two while maintaining the same accuracy as the best conventional sampling interval. The results illustrate that adaptive sampling provides the potential for better monitoring, control, and management of high-performance networks with higher accuracy, lower overhead, or both.

Byte Segment Neural Network for Network Traffic Classification

Abstract: Network traffic classification, which can map network traffic to protocols in the application layer, is a fundamental technique for network management and security issues such as Quality of Service, network measurement, and network monitoring. Recent researchers focus on extracting features for traditional machine learning methods from flows or datagrams of the specific protocol. However, as the rapid growth of network applications, previous works cannot handle complex novel protocols well. In this paper, we introduce the recurrent neural network to network traffic classification and design a novel neural network, the Byte Segment Neural Network (BSNN). BSNN treats network datagrams as input and gives the classification results directly. In BSNN, a datagram is firstly broken into serval byte segments. Then, these segments are fed to encoders which are based on the recurrent neural network. The information extracted by encoders is combined to a representation vector of the whole datagram. Finally, we apply the softmax function to use this vector for predicting the application protocol of this datagram. There are several key advantages of BSNN: 1) no need for prior knowledge of target applications; 2) can handle both connection-oriented protocols and connection-less protocols; 3) supports multi-classification for protocols; 4) shows outstanding accuracy in both traditional protocols and complex novel protocols. Our thorough experiments on real-world data with different protocols indicate that BSNN gains average F1-measure about 95.82% in multi-classification for five protocols including QQ, PPLive, DNS, 360 and BitTorrent. And it also shows excellent performance for detection of novel protocols. Furthermore, compared with two recent state-of-the-art works, BSNN has superiority over the traditional machine learning-based method and the packet inspection method.