781,596 research outputs found
Evolution towards Smart Optical Networking: Where Artificial Intelligence (AI) meets the World of Photonics
Smart optical networks are the next evolution of programmable networking and
programmable automation of optical networks, with human-in-the-loop network
control and management. The paper discusses this evolution and the role of
Artificial Intelligence (AI)
Multifaceted Faculty Network Design and Management: Practice and Experience Report
We report on our experience on multidimensional aspects of our faculty's
network design and management, including some unique aspects such as
campus-wide VLANs and ghosting, security and monitoring, switching and routing,
and others. We outline a historical perspective on certain research, design,
and development decisions and discuss the network topology, its scalability,
and management in detail; the services our network provides, and its evolution.
We overview the security aspects of the management as well as data management
and automation and the use of the data by other members of the IT group in the
faculty.Comment: 19 pages, 11 figures, TOC and index; a short version presented at
C3S2E'11; v6: more proofreading, index, TOC, reference
The Evolution of OSI Network Management by Integrated the Expert Knowledge
The management of modern telecommunications networks must satisfy
ever-increasing operational demands. Operation and quality service requirements
imposed by the users are also an important aspect to consider. In
this paper we have carried out a study for the improvement of intelligent administration
techniques in telecommunications networks. This task is achieved
by integrating knowledge base of expert system within the management information
used to manage a network. For this purpose, an extension of OSI management
framework specifications language has been added and investigated
in this study. A new property named RULE has also been added, which gathers
important aspects of the facts and the knowledge base of the embedded
expert system. Networks can be managed easily by using this proposed integration
Intelligent Resource Management for Local Area Networks: Approach and Evolution
The Data Management System network is a complex and important part of manned space platforms. Its efficient operation is vital to crew, subsystems and experiments. AI is being considered to aid in the initial design of the network and to augment the management of its operation. The Intelligent Resource Management for Local Area Networks (IRMA-LAN) project is concerned with the application of AI techniques to network configuration and management. A network simulation was constructed employing real time process scheduling for realistic loads, and utilizing the IEEE 802.4 token passing scheme. This simulation is an integral part of the construction of the IRMA-LAN system. From it, a causal model is being constructed for use in prediction and deep reasoning about the system configuration. An AI network design advisor is being added to help in the design of an efficient network. The AI portion of the system is planned to evolve into a dynamic network management aid. The approach, the integrated simulation, project evolution, and some initial results are described
Traffic Management Applications for Stateful SDN Data Plane
The successful OpenFlow approach to Software Defined Networking (SDN) allows
network programmability through a central controller able to orchestrate a set
of dumb switches. However, the simple match/action abstraction of OpenFlow
switches constrains the evolution of the forwarding rules to be fully managed
by the controller. This can be particularly limiting for a number of
applications that are affected by the delay of the slow control path, like
traffic management applications. Some recent proposals are pushing toward an
evolution of the OpenFlow abstraction to enable the evolution of forwarding
policies directly in the data plane based on state machines and local events.
In this paper, we present two traffic management applications that exploit a
stateful data plane and their prototype implementation based on OpenState, an
OpenFlow evolution that we recently proposed.Comment: 6 pages, 9 figure
Active local distribution network management for embedded generation
Traditionally, distribution networks have been operated as passive networks with uni-directional power flows. With the connection of increasing amounts of distributed generation, these networks are becoming active with power flowing in two directions, hence requiring more intelligent forms of management. The report into issues for access to electricity networks published by the Ofgem/DTI Embedded Generation Working Group in January 2001 called for new work in the area of active distribution network management. The report suggested an evolution from the present passive network control philosophy to fully active network control methods. In line with these recommendations Econnect is developing a new type of distribution network controller, called GenAVC. GenAVC is a controller for electricity distribution networks that aims to increase the amount of energy that can be exported onto the distribution networks by generating plants. The UK is leading the world in electricity de-regulation and one aspect of this is the increasing demand for the connection of distributed generation. Active distribution network management is seen to be essential for networks to accommodate the levels of distributed generation that are predicted for 2010. The work being undertaken as part of this project is therefore at the forefront of international network management technology
On the security of software-defined next-generation cellular networks
In the recent years, mobile cellular networks are ndergoing fundamental changes and many established concepts are being revisited. Future 5G network architectures will be designed to employ a wide range of new and emerging technologies such as Software Defined Networking (SDN) and Network Functions Virtualization (NFV). These create new virtual network elements each affecting the logic of the network management and operation, enabling the creation of new generation services with substantially higher data rates and lower delays. However, new security challenges and threats are also introduced. Current Long-Term Evolution (LTE) networks are not able to accommodate these new trends in a secure and reliable way. At the same time, novel 5G systems have proffered invaluable opportunities of developing novel solutions for attack prevention, management, and recovery. In this paper, first we discuss the main security threats and possible attack vectors in cellular networks. Second, driven by the emerging next-generation cellular networks, we discuss the architectural and functional requirements to enable
appropriate levels of security
SDN Access Control for the Masses
The evolution of Software-Defined Networking (SDN) has so far been
predominantly geared towards defining and refining the abstractions on the
forwarding and control planes. However, despite a maturing south-bound
interface and a range of proposed network operating systems, the network
management application layer is yet to be specified and standardized. It has
currently poorly defined access control mechanisms that could be exposed to
network applications. Available mechanisms allow only rudimentary control and
lack procedures to partition resource access across multiple dimensions.
We address this by extending the SDN north-bound interface to provide control
over shared resources to key stakeholders of network infrastructure: network
providers, operators and application developers. We introduce a taxonomy of SDN
access models, describe a comprehensive design for SDN access control and
implement the proposed solution as an extension of the ONOS network controller
intent framework
Predictive genomics: A cancer hallmark network framework for predicting tumor clinical phenotypes using genome sequencing data
We discuss a cancer hallmark network framework for modelling
genome-sequencing data to predict cancer clonal evolution and associated
clinical phenotypes. Strategies of using this framework in conjunction with
genome sequencing data in an attempt to predict personalized drug targets, drug
resistance, and metastasis for a cancer patient, as well as cancer risks for a
healthy individual are discussed. Accurate prediction of cancer clonal
evolution and clinical phenotypes will have substantial impact on timely
diagnosis, personalized management and prevention of cancer.Comment: 5 figs, related papers, visit lab homepage:
http://www.cancer-systemsbiology.org, Seminar in Cancer Biology, 201
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