Showing papers on "Network management published in 2006"

Deploying a wireless sensor network on an active volcano

Abstract: Augmenting heavy and power-hungry data collection equipment with lighten smaller wireless sensor network nodes leads to faster, larger deployments. Arrays comprising dozens of wireless sensor nodes are now possible, allowing scientific studies that aren't feasible with traditional instrumentation. Designing sensor networks to support volcanic studies requires addressing the high data rates and high data fidelity these studies demand. The authors' sensor-network application for volcanic data collection relies on triggered event detection and reliable data retrieval to meet bandwidth and data-quality demands.

A preliminary performance comparison of five machine learning algorithms for practical IP traffic flow classification

Abstract: The identification of network applications through observation of associated packet traffic flows is vital to the areas of network management and surveillance. Currently popular methods such as port number and payload-based identification exhibit a number of shortfalls. An alternative is to use machine learning (ML) techniques and identify network applications based on per-flow statistics, derived from payload-independent features such as packet length and inter-arrival time distributions. The performance impact of feature set reduction, using Consistency-based and Correlation-based feature selection, is demonstrated on Naive Bayes, C4.5, Bayesian Network and Naive Bayes Tree algorithms. We then show that it is useful to differentiate algorithms based on computational performance rather than classification accuracy alone, as although classification accuracy between the algorithms is similar, computational performance can differ significantly.

Virtual network management method, virtual network management program, virtual network management system, and virtual network means

Abstract: The invention relates to a network management method wherein: a service management equipment holds the mapping of services and virtual networks as management information; a managed server sends an advice to a VLAN management equipment when its status changes; and the VLAN management equipment receives the advice and refers to the information contained in the advice to thereby identify the managed server and the connection port of a network switch, and configures a virtual network belonging to the identified port of the network switch. The method alleviates the task of reconfiguration associated with the adding or deleting of servers.

A Typology of Plants in Global Manufacturing Networks

Abstract: The purpose of this paper is to propose a new, empirically derived typology of plants in the international manufacturing network of multinational companies. This typology is based on the knowledge flows between the plants. In our research, network analysis has been used as a methodology for understanding the position of plants in international manufacturing networks. The focus has been primarily on the intangible knowledge network, and secondarily on the physical, logistic network. Our analysis leads to four types of plants with different network roles: the isolated plants, the receivers, the hosting network players, and the active network players. Our analysis shows that the different types of plants play a different strategic role in the company, have a different focus, and differ in age, autonomy, and level of resources and investments. Also, the analysis suggests that the evolution of the plant depends to some extent on the network role of the plant. Finally, two scenarios for the development of a strong network role are identified. The research is useful for the scholar studying the architecture of knowledge networks, as well as for the practitioner who is in charge of an international network of manufacturing units.

The Multisectoral Trilemma of Network Management

Abstract: This article presents an analysis of different network coordination strategies. The article extends network management scholarship by integrating sector-based differences within a theoretical framework encompassing extant conceptions of network management. Even as the emergent field of network management scholarship advances, current research tends to generalize network management approaches based on assumptions that organizations behave similarly within a network regardless of whether the organizations are governmental, nonprofit, or commercial. Consequently, existing research does not fully account for whether sector-based differences have implications for network management. This article provides evidence that sector-based differences within a network matter because the differences provide strategic opportunities and constraints for managers involved in coordinating mixed-sector networks. This article makes several contributions to network management scholarship. First, this article provides a framework that reviews and situates current conceptions about network coordination within a passive-to-active continuum of managerial approaches. Sectoral differences are situated and integrated within this framework. Second, this article provides an empirically based investigation of a quasi-natural experiment that examines sector-based differences in mixed-sector workforce development networks in Boston. The article's findings suggest that integrating sector-based orientations within a passive-to-active network managerial continuum helps clarify and categorize the strategic options and trade-offs that managers may consider in coordinating multisectoral networks.

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.

Network Management Fundamentals

Abstract: Network Management FundamentalsA guide to understanding how network management technology really worksAlexander Clemm, Ph.D.Network management is an essential factor in successfully operating a network. As a company becomes increasingly dependent on networking services, keeping those services running is synonymous with keeping the business running. Network Management Fundamentals provides you with an accessible overview of network management covering management not just of networks themselves but also of services running over those networks.Network Management Fundamentals explains the different technologies that are used in network management and how they relate to each other. The book focuses on fundamental concepts and principles. It provides a solid technical foundation for the practitioner to successfully navigate network management topics and apply those concepts to particular situations.The book is divided into four parts: Part I provides an overview of what network management is about and why it is relevant. It also conveys an informal understanding of the functions, tools, and activities that are associated with it. Part II examines network management from several different angles, culminating in a discussion of how these aspects are combined into management reference models. Part III provides more detail into different building blocks of network management introduced in Part II, such as management protocols, management organization, and management communication patterns.i¾ Part IV rounds out the book with a number of management topics of general interest, including management integration and service-level management.Dr. Alexander Clemm is a senior architect with Cisco®. He has been involved with integrated management of networked systems and services since 1990. He has provided technical leadership for many leading-edge network management development, architecture, and engineering efforts from original conception to delivery to the customer, and he has also served as technical program co-chair of the 2005 IFIP/IEEE International Symposium on Integrated Network Management. Grasp the business implications of network management Examine different management reference models, such as Fault, Configuration, Accounting, Performance, and Security (FCAPS) Understand the building blocks of network management and their purposes Assess the implications and impact of management technologies and put them in perspective Prepare for decisions about network management that require an understanding of the “big picture”This book is part of the Cisco Press® Fundamentals Series. Books in this series introduce networking professionals to new networking technologies, covering network topologies, example deployment concepts, protocols, and management techniques.Category: NetworkingCovers: Network Management$55.00 USA / $69.00 CAN

Selection and storage of policies in network management

Abstract: User interface and policy loading aspects of a policy-based, outsourced, network management system. In one aspect, a user selects policies using a graphical user interface (GUI) with a two paned window having a tree view of the policies in one pane. In another aspect, the policies are (1) created in the GUI format (e.g., XML), (2) sent over a network (e.g., the internet) to a service center in the same format, and (3) are loaded, manipulated and stored in the same format. In another aspect, the initial loading of the policies is done using a bulk loader in a logic layer. In another aspect, the logic layer also includes a configuration checker which handles changes or additions to policies in a finished network management system. Any aspects of the new or changed policy that are inconsistent with the finished system are parsed and stripped out. In another aspect, where the details of a new policy or change aren't specified, a base configuration creator creates a policy with minimal attributes. In another aspect, the logic layer also contains a device control console, which allows bypassing the policy creating and configuring to allow a user to directly access a device for configuration.

RTWPMS: A Real-Time Wireless Physiological Monitoring System

Abstract: This paper demonstrates the design and implementation of a real-time wireless physiological monitoring system for nursing centers, whose function is to monitor online the physiological status of aged patients via wireless communication channel and wired local area network. The collected data, such as body temperature, blood pressure, and heart rate, can then be stored in the computer of a network management center to facilitate the medical staff in a nursing center to monitor in real time or analyze in batch mode the physiological changes of the patients under observation. Our proposed system is bidirectional, has low power consumption, is cost effective, is modular designed, has the capability of operating independently, and can be used to improve the service quality and reduce the workload of the staff in a nursing center

Network device management

Abstract: A network device management tool that allows a client, such as a network management tool or network device setup utility, to reliably obtain information about a network device Variations of the network device management tool may alternately or additionally allow a client to deliver information to the network device, such as instructions to control the operation of the network device The network device management tool may be incorporated into the network device

Apparatus and method for wireless audio network management

Abstract: Electronic apparatus and method are provided for management of communications between channel master (e.g. audio player), responding device (e.g. remote control/headphones) and, optionally, passive devices (e.g. headphones) wirelessly connected in a wireless audio network. The channel master transmits, and the responding device receives, audio signals and network management information. Profile negotiation means communicates application profiles for the devices, establishing at least one application profile which is common to them and designating a selected, common application profile for use in associating the devices. Channel selection means selects a channel for the communications using an ordered selection of channels (PCS) obtained from scanning the channels for idle channels and ordering the scanned channels according to priority based on channel quality. Normalized control and status interfaces establish interoperability between devices which use different data streams to communicate in the network.

Surveillance network system

Abstract: Embodiments of a sensor network system provide surveillance capabilities in multiple contexts/environments (eg, military, commercial, scientific, civic, urban, wilderness, etc) Network nodes may include devices such as sensors, network routers, network controllers, etc Network sensors may be configured so that power management objectives are maximized Network sensors (both individually and as a group) may be capable of intelligent and cooperative information gathering, so that the output of the sensor network does not contain high levels of irrelevant information The network nodes may communicate among one another via one or more communication links, and in some cases, multiple routes between any two network nodes may be available The sensor network may include aspects of both high data rate and low data rate network features One or more network controllers may provide various network management capabilities, including management of network routing, information collection, information exportation, network configuration, etc

Graphical user interface for network management

Abstract: A graphical user interface (GUI) provides a plurality of views of a network and its elements in the same viewing engine. A user can switch between the plurality of views in a context-sensitive manner, each view showing relationship or interconnection information. The GUI allows a user to view inter-related objects at the same level, and to view at a lower level sub-objects that make up each of those objects. Different functional views can be provided at the same hierarchical or logical level based on the stored relationship information. A user can navigate between a network level view, a site level view, a shelf level view, and a schematic level view, via element selection or by zooming. A network element data set provides context-sensitive data and images to each level and view for that network element and enables automatic generation of a network topology.

Method and system for dynamic loading of management information bases on network devices

Abstract: In one embodiment, a Management Information Base (MIB) is dynamically loaded on a managed network device that is managed by a managing network device using a management application. Initially, one or more network management objects are identified. The identified one or more network management objects are then included in the MIB. Thereafter, a policy file to be associated with the MIB is defined. Consequently, the MIB is loaded on the managed network device along with the associated policy file.

Fault and attack management in all-optical networks

Abstract: Network management for optical networks faces additional security challenges that arise by using transparent optical network components in communication systems. While some available management mechanisms are applicable to different types of network architectures, many of these are not adequate for all-optical networks. These have unique features and requirements in terms of security and quality of service, thus requiring a much more targeted approach in terms of network management. In this article we consider management issues with particular emphasis on complications that arise due to the unique characteristics and peculiar behaviors of transparent network components. In particular, signal quality monitoring is still a major complication in all-optical networks. Despite new methods for detection and localization of attacks having been proposed, no robust standards or techniques exist to date for guaranteeing the quality of service in these networks. Therefore, sophisticated mechanisms that assist in managing and assessing the proper function of transparent network components are highly desirable. Accordingly, we present an algorithm for multiple attack localization and identification that can participate in some tasks for fault management of all-optical networks

Multiple-Source Internet Tomography

Abstract: Information about the topology and link-level characteristics of a network is critical for many applications including network diagnostics and management. However, this information is not always directly accessible; subnetworks may not cooperate in releasing information and widespread local measurement can be prohibitively expensive. Network tomographic techniques obviate the need for network cooperation, but the majority assume probing from a single source, which imposes scalability limitations because sampling traffic is concentrated on network links close to the source. We describe a multiple source, end-to-end sampling architecture that uses coordinated transmission of carefully engineered multipacket probes to jointly infer logical topology and estimate link-level performance characteristics. We commence by demonstrating that the general multiple source, multiple destination tomography problem can be formally reduced to the two source, two destination case, allowing the immediate generalization of any sampling techniques developed for the simpler, smaller scenario. We then describe a method for testing whether links are shared in the topologies perceived by individual sources, and describe how to fuse the measurements in the shared case to generate more accurate estimates of the link-level performance statistics

Method, device and system for monitoring network performance

Abstract: A method for monitoring network performance includes: sending correspondences between a remote network element and a plurality of IP addresses thereof from a network management device to a local network element; according to the correspondences, calculating performance parameters between the local IP address and the plurality of remote IP addresses respectively, by the local network element; making a statistics of the calculated performance parameters by the local network element. A network element for monitoring network performance, connected with a network management device and a remote network element, includes: a receiving unit, a performance parameter processing unit, a performance parameter statistics unit, and the transmitting unit. A network system for monitoring network performance is further provided. According to embodiments of this invention, the efficient measurement of network performance including IP QoS between each two MGWs of an IP network, RTP stream bandwidths, and the like, is realized, thus the message bandwidth of performance test can be saved and processing load of the network management device can be reduced.

Wireless sensor network energy-adaptive mac protocol

Abstract: Wireless Sensor Networks (WSNs) provide a valuable capability to autonomously monitor remote activities. Their limited resources challenge WSN medium access control (MAC) layer designers to adequately support network services while conserving limited battery power. This paper presents an energyadaptive WSN MAC protocol, Gateway MAC (GMAC), which implements a new cluster-centric paradigm to effectively distribute cluster energy resources and extend network lifetime. G-MAC’s centralized cluster management function offers significant energy savings by leveraging the advantages of both contention and contention-free protocols. A centralized gateway node collects all transmission requirements during a contention period and then schedules their distributions during a reservation-based, contention-free period. With minimal overhead, the gateway duties are efficiently rotated based upon available resources to distribute the increased network management energy requirements among all of the nodes.

Communication apparatus and communication method

Abstract: In a communication apparatus on a network where communication apparatuses perform direct communication with each other, a network management apparatus for managing the network based on message information transmitted and received on the network is determined. When the communication apparatus itself is determined as the network management apparatus, the apparatus collects information indicating device capabilities from other communication apparatuses. On the other hand, the communication apparatus receives information indicating device capabilities from another communication apparatus when the other communication apparatus is determined as the network management apparatus.

Network management method and communications network system

Abstract: This invention provides an improved data transferring method performed in a communication network comprising a management system M1 and a plurality of nodes K1-K3. Since the management system M1 collectively manages the algorithm information that indicates algorithm possessed in each node, if the fist node does not have algorithm needed for processing a certain packet, the first node can find the second node having the algorithm by inquiring the network management element M1. In addition, the first node can transfer the certain packet to the second node to process the certain packet by using the algorithm.

Weibull mixture model to characterise end-to-end Internet delay at coarse time-scales

Abstract: Traces collected at monitored points around the Internet contain representative performance information about the paths their probes traverse. If processed appropriately, basic measurement attributes, such as delay and loss, can be used to output conclusions about the performance status of the network, with subsequent applications in fault and performance management, network provisioning, traffic engineering and performance prediction. However, the task of analysis and extracting such valuable information from measurements only remains challenging. The Weibull mixture model, a method to characterise end-to-end network delay measurements within a few simple, accurate, representative and handleable parameters using a finite combination of Weibull distributions is presented. The model parameters are related to meaningful delay characteristics, such as average peak and tail behaviour in a daily profile, and can be optimally found using an iterative algorithm known as expectation maximisation. Studies on such parameter evolution can reflect current workload status and all possible network events impacting packet dynamics, with further applications in network management. The model is further tested and validated with real GPS synchronised measurements taken across the Internet, donated by RIPE NCC.

Evaluating machine learning algorithms for automated network application identification

Abstract: The identification of network applications that create traffic flows is vital to the areas of network management and surveillance. Current popular methods such as port number and payload-based identification are inadequate and exhibit a number of shortfalls. A potential solution is the use of machine learning techniques to identify network applications based on payload independent statistical features. In this paper we evaluate and compare the efficiency and performance of different feature selection and machine learning techniques based on flow data obtained from a number of public traffic traces. We also provide insights into which flow features are the most useful. Furthermore, we investigate the influence of other factors such as flow timeout and size of the training data set. We find significant performance differences between different algorithms and identify several algorithms that provide accurate (up to 99% accuracy) and fast classification. Keywords—Traffic Classification, Machine Learning, Statistical Features I. INTRODUCTION There is a growing need for accurate and timely classification of network traffic flows for purposes such as trend analyses (estimating capacity demand trends for network planning), adaptive, network-based QoS marking of traffic, dynamic access control (adaptive firewalls that detect forbidden applications or attacks) or lawful interception. 'Classification' refers to the identification of an application or group of applications responsible for a traffic flow. Port-based classification is still widely practiced despite being only moderately accurate at best. It is expected to become less effective in the near future due to an ever-increasing number of network applications, extensive use of network address translation (NAT), dynamic port allocation and end-users deliberately choosing non-default ports. For example a large amount of peer-to-peer (p2p) file sharing traffic is found on non- default ports (1). Alternative solutions such as payload- based classification rely on specific application data (protocol decoding or signatures), making it difficult to

A P2P-based framework for distributed network management

Abstract: In this paper we present a novel framework supporting distributed network management using a self-organizing peer-to-peer overlay network. The overlay consists of several Distributed Network Agents which can perform distributed tests and distributed monitoring for fault and performance management. In that way, the concept is able to overcome disadvantages that come along with a central management unit, like lack of scalability and reliability. So far, little attention has been payed to the quality of service experienced by the end user. Our self-organizing management overlay provides a reliable and scalable basis for distributed tests that incorporate the end user. The use of a distributed, self-organizing software will also reduce capital and operational expenditures of the operator since fewer entities have to be installed and operated.

Challenges for Autonomic Network Management

Abstract: The improvement in the management capabilities and available bandwidth offered by next generation of networks is accelerating the development of a new kind of applications, interfaces and services. The ultimate goal of such networks is to automatically adapt their services and resources in accordance with changing environmental conditions and user needs. Such ‘autonomic’ capabilities imply the usage of sophisticated technologies in order to integrate every object of our environment that would be enabled with important computational power and storage capabilities. The challenge behind such implementation is to simplify the administrators’ task by automating the decision making process, and enabling the users to seamlessly find their way in such pervasive environments. This paper gives an overview of the different architectures that support the design, implementation and deployment of autonomic systems.

CSSE Technical Report UWA-CSSE-06-001 June 2006 WinMS: Wireless Sensor Network-Management System, An Adaptive Policy-Based Management for Wireless Sensor Networks

Abstract: Wireless sensor networks are highly dynamic, prone to faults, and typically deployed in remote and harsh conditions. We propose WinMS, an adaptive policy-based sensor network management system that provides self-management for maintaining the performance of the network and achieving eective networked node operations without human intervention. WinMS adapts to changing network conditions by allowing the network to reconfigure itself according to current events as well as predicting future events. WinMS architecture consists of a schedule-driven MAC protocol that collects and disseminates management data, to and from sensor nodes in a data gathering tree, a local network management scheme that provides autonomy for individual sensor nodes to perform management functions according to their neighborhood network states, and a central network management scheme that uses the central manager with a global knowledge of the network to reliably execute corrective and preventive management maintenance. WinMS proposes a novel management function, called systematic resource transfer, that allows time slots (resources) from one part of the network to be transferred to another part. This paper presents analysis and simulation results of the proposed function, showing that it supports non-uniform and reactive sensing in dierent parts of a network.