Network Topology

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Computer Network Topology

Computer Network Topology

Abstract: In recent day’s demands of high productivity, security of computer systems and computer networks is very important and popular issue. Communication network in a distributed computing environment, which is arranged in a geometrical shape, called network topology. There are different types of the topologies like bus, ring, star, tree, mesh, hybrid etc. However, we will consider five basic network structures- topology. In the present paper a detailed study and analysis on network topologies is presented. Definitions of physical and logical topology are also provided.
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Analysis on Computer Network Topology for Store and Transmission Channel

Analysis on Computer Network Topology for Store and Transmission Channel

A daisy-chained network topology is all of your devices are connected in a chain- like or ring fashion. The master controller connects to a slave device, which in turn connects to another slave device, which connects to another slave device, and so on, and so on. If the ring breaks at a particular link then the transmission can be sent via the reverse path thereby ensuring that all nodes are always connected in the case of a single failure. A major disadvantage is a component failure or cable failure in midstream will disable the entire network and if you want to add a device in the middle of the chain or ring, the network will get down during the process. The cabling for these networks is generally put in open space and may therefore be more vulnerable to accidental disconnections and breaks.
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Continuous Learning of a Multilayered Network Topology in a Video Camera Network

Continuous Learning of a Multilayered Network Topology in a Video Camera Network

A multilayered camera network architecture with nodes as entry/exit points, cameras, and clusters of cameras at different layers is proposed. Unlike existing methods that used discrete events or appearance information to infer the network topology at a single level, this paper integrates face recognition that provides robustness to appearance changes and better models the time-varying traffic patterns in the network. The statistical dependence between the nodes, indicating the connectivity and traffic patterns of the camera network, is represented by a weighted directed graph and transition times that may have multimodal distributions. The traffic patterns and the network topology may be changing in the dynamic environment. We propose a Monte Carlo Expectation- Maximization algorithm-based continuous learning mechanism to capture the latent dynamically changing characteristics of the network topology. In the experiments, a nine-camera network with twenty-five nodes (at the lowest level) is analyzed both in simulation and in real-life experiments and compared with previous approaches.
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Localizing Node Failures in Network Topology and Locations of Monitors

Localizing Node Failures in Network Topology and Locations of Monitors

We studied the fundamental capability of a network in localizing failed nodes from binary measurements (normal/failed) of paths between monitors. We proposed two novel measures: maximum identifiability index that quantifies the scale of uniquely localizable failures wrt a given node set, and maximum identifiable set that quantifies the scope of unique localization under a given scale of failures. We showed that both measures are functions of the maximum identifiability index per node. We studied these measures for three types of probing mechanisms that offer different controllability of probes and complexity of implementation. For each probing mechanism, we established necessary/sufficient conditions for unique failure localization based on network topology, placement of monitors, constraints on measurement paths, and scale of failures. We further showed that these conditions lead to tight upper/lower bounds on the maximum identifiability index, as well as inner/outer bounds on the maximum identifiable set. We showed that both the conditions and the bounds can be evaluated efficiently using polynomialtime algorithms. Our evaluations on random and real network topologies showed that probing mechanisms that allow monitors to control the routing of probes have significantly better capability to uniquely localize failures.
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Molecular network topology and reliability for multipurpose diagnosis

Molecular network topology and reliability for multipurpose diagnosis

Abstract: This investigation proposes the use of molecular network topology for drug delivery and diagnosis network design. Three modules of molecular network topologies, such as bus, star, and ring networks, are designed and manipulated based on a micro- and nanoring resonator system. The transportation of the trapping molecules by light in the network is described and the theoretical background is reviewed. The quality of the network is analyzed and calculated in terms of signal transmission (ie, signal to noise ratio and crosstalk effects). Results obtained show that a bus network has advantages over star and ring networks, where the use of mesh networks is possible. In application, a thin film network can be fabricated in the form of a waveguide and embedded in artificial bone, which can be connected to the required drug targets. The particular drug/nutrient can be transported to the required targets via the particular network used. Keywords: molecular network, network reliability, network topology, drug network, multi- access network
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TopoMon: A Monitoring Tool for Grid Network Topology

TopoMon: A Monitoring Tool for Grid Network Topology

TopoMon currently conservatively underestimates the bandwidth of net- work links, neglecting the possibly higher backbone bandwidth that might be observed by multiple, simultaneous transmissions. This is a direct consequence of using NWS for network measurements. NWS carefully avoids simultaneous measurements both for minimizing intrusiveness and for accuracy [22]. We are currently working on a scheme to augment NWS to additionally perform con- current measurements that both explores the potential of the Internet backbone and minimizes the necessary, additional measurements. Exploiting the topology of the given network connections is key to this scheme. We are also working on a better graphical representation of the network topology to visualize conflict- ing data streams. Finally, we are augmenting our MagPIe library with collective communication algorithms that exploit the information derived by TopoMon.
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Dynamically Adjusting Network Topology for MANETS By using DBET

Dynamically Adjusting Network Topology for MANETS By using DBET

The simulation studies revealed that the proposed scheme perform better in terms of energy, delay, and delivery ratio. In general network topology is controlled by keeping small number of nodes awake as in the first technique. The proposed DBET keeps more number of nodes along the bulk data transfer path to conserve energy by keeping low link cost as in the second technique. The rest of the paper is organized as follows: the next section provides a brief review of related studies. The third section gives the design details of proposed DBET. Integration issues of DBET with routing protocol is discussed in the forth section. Simulation results along with discussions are provided in the section 5. The last and final section concludes the paper with same pointers to future research direction. AODV Routing Protocol
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Network topology of renewable energy sector in stock exchange

Network topology of renewable energy sector in stock exchange

With reference to the paramount role of Network Analysis which provokes the representation of financial markets, the network topology essentially improves the understanding of structural. Stock markets have performed Innumerable analysis of stock markets (Mantegna, 1999; Bonanno et al., 2004; Coelho et al., 2007). Moreover, in accordance with the growth of renewable energy, multiple companies involved in these sectors have emerged. Their presence on stock exchanges worldwide has fueled the creation of indices that aims to track the performance of renewable, allowing us to analyze how this sector performs compared to other assets. However, the branch of the renewable energy has not been highlighted yet. Therefore, the present research considers the 62 renewable energy stocks from 30th February 2015 to 3th March 2016 to the analysis of the renewable energy exchange market by using network theory. This approach helps us to analyze the interaction between stocks and the performances of them that determine the level of importance to find significant implications in stock market structure.
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The Effect of Network Topology on the Spread of Epidemics

The Effect of Network Topology on the Spread of Epidemics

Many network phenomena are well modeled as spreads of epidemics through a network. Prominent examples include the spread of worms and email viruses, and, more generally, faults. The spread of informa- tion can also often be modeled as the spread of an epidemic. An epidemic spreads along the underlying network topology from an initial set of infected nodes to susceptible nodes. In many cases infected nodes can be cured, reverting back to being susceptible and possibly

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Impact of Transmission Network Topology on Electrical Power Systems

Impact of Transmission Network Topology on Electrical Power Systems

Compared to the aforementioned system parameters, the role of transmission net- work topology on the transient stability of swing dynamics is less well understood. Indeed, it is usually hard to infer how a change to the network topology affects overall grid behavior and performance without detailed simulation and computa- tion. For example, one can argue that the connectivity in the grid helps average the power demand imbalance over the network, and therefore higher connectivity should enhance system stability. On the other hand, one can also argue that higher con- nectivity means faster propagation of disturbances over the network, which should therefore decrease system stability. Both arguments seem plausible but they lead to (apparently) opposite conclusions (a corollary of our results in Section 3.2 will clarify this paradox). In fact, even the notion “connectivity” itself seems vague and is open to different interpretations.
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Resource-Aware Network Topology Management Framework

Resource-Aware Network Topology Management Framework

SDN concepts bring great potential and efficiency to reduce the complexity of network control. To efficiently manage and configure the network, it needs to have up-to-date information about the state of the network, in particular, its topology. The paper presents a network topology framework. It utilizes SDN–enabled infrastructure for improving topology management functions. The proposed framework employs SLAs, PCE and shares fair loading as a means for improving resource administration. The framework facilitates in reducing complexities for resource allocation problems. We evaluate our system on limited real-time controlled data center traffic. We believe that the presented work provides a foundation for developing a more efficient topology management infrastructure. In the future, we plan to improve our system’s planner design so that it can effectively handle VM placement and allocation related challenges. We also plan to reduce VM overheads by improving the topology discovery feature in a data center environment.
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Influence of network topology on sound propagation in granular materials

Influence of network topology on sound propagation in granular materials

We investigate whether studying the rich and complex dynamics of granular materials [7] using network analysis can provide new insights into the underlying physics. This treatment is a natural one, because granular materials can be represented as spatially embedded networks [8] composed of nodes (particles) and edges (contacts between particles) with definite locations in Euclidean space [9,10]. In Fig. 1, we show a quasi-two-dimensional granular system composed of photoelastic disks, which permits the determination of both the contact network and the interparticle forces. The forces between particles in these systems are nonhomogeneous, and they form a network of chain-like structures that span the system [see Fig. 1(b)]. This force-chain network has the same topology as the contact network but contains edges that are weighted by the interparticle forces [Fig. 1(c)]. This is exciting from a networks perspective, as it allows us to study the influence of network topology on “network geometry”
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Control of vein network topology by auxin transport

Control of vein network topology by auxin transport

In contrast to the control of vein network topology by intracellular auxin transport, no genetic evidence is available in support of a role for the cell-to-cell transport of auxin in control of vein network topology; yet such a role seems to be suggested by imaging and inhibitor studies. Expression of the PIN1 auxin efflux protein [27, 35] is initiated in broad domains of leaf inner cells that be- come gradually restricted to files of vascular precursor cells in contact with pre-existing, narrow PIN1 expression domains [33, 36–42]. Within broad expression domains, PIN1 is localized isotropically—or nearly so—to the plasma membrane (PM) of leaf inner cells. As expression of PIN1 becomes gradually restricted to files of vascular precursor cells, PIN1 localization becomes polarized to the side of the PM facing the pre-existing, narrow PIN1 expression domains with which the narrowing domains are in contact. Initially, PIN1 expression domains are in contact with pre-existing domains at one end only, but they can eventually become connected to other PIN1 ex- pression domains at both ends. Inhibitors of cellular auxin efflux delay the restriction of PIN1 expression domains and the polarization of PIN1 localization [38, 39], and induce the formation of more veins [4, 5].
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Effect of Network Topology on Relative Permeability; Network Model and Experimental Approaches

Effect of Network Topology on Relative Permeability; Network Model and Experimental Approaches

Abstract: The effect of topological properties on imbibition relative permeabilities and residual saturations was previously studied by utilizing quasi-static network model topologies extracted from actual sandstones 3D micro-tomographic images. Non-wetting fluid in imbibition displacements can be disconnected by snap-off as a result of swelling of wetting films in the corners of pores and throats. The findings showed that the effect of topology on imbibition relative permeabilities depends on the level of snap-off. For strongly wetting conditions where snap-off dominates the displacement the effect of network topology is significantly smaller than for weakly wet conditions where snap-off is suppressed. The findings were valid for random networks and for networks displaying short-range pore-throat and longer-range spatial correlations. The aim of this study is to validate network model findings by comparing them with laboratory measurements of relative permeabilities. Laboratory measured data include imbibition relative permeability for sandstones of similar petrophysical properties to Fontainebleau sandstone used to extract 3D micro-tomographic images. Laboratory measurements were made at ambient conditions on core samples of different diameters and different porosities and permeabilities. Experimental measurements were in good qualitative agreement with stochastic networks that match the full coordination number distribution and geometric properties of networks obtained from 3D micro-CT images. Experimental measurements were also in good agreement with networks displaying both short-range pore-throat correlations and longer-range spatial correlations.
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Finding Ethernet-Type Network Topology is Not Easy

Finding Ethernet-Type Network Topology is Not Easy

Layer-2 network topology problems were addressed in the research community by several researchers [2], [4], [5], [6], [12], [17], [18]. For the set of complete AF T s the algorithms from [3], [5], [6] find the layer-2 topology for multisubnet net- works. In [5] the authors observed that for multisubnet net- works the network topology may not be unique even for the set of complete AF T s. In such a case finding an exact topol- ogy is not possible and algorithm from [6] generates some net- work fragments that can be uniquely determined and supplies the network manager with a set of possible topologies. In [10] a criterion was introduced on the set of complete AF T s guar- anteeing a unique topology for multisubnet networks. Bejer- ano [4] proposed a very simple layer-2 topology restoration method for multisubnet networks. While his method restores a layer-2 network topology in a wide variety of cases, it cannot guarantee a topology restoration. His method also requires a completeness of input AF T s.
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Modeling wireless network topology with NS3

Modeling wireless network topology with NS3

We provide an entire script and examine some of the output. Take a look at the ASCII art reproduced below in figure-1 that shows the WiFi network topology constructed. You can see that we are going to further extend our example by hanging a wireless network off of the left side. Notice that this is a default network topology since you can actually vary the number of nodes created on the wired and wireless networks.

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Review Paper on Network Topology

Review Paper on Network Topology

When two or more devices are connected to each other through connecting links, then it is known as a network. Network topology describes the layout or appearance of a network that is, how the computers, cables, and other components within a data communication network are interconnected, both physically and logically.

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Effects of local network topology on the functional reconstruction of spiking neural network models

Effects of local network topology on the functional reconstruction of spiking neural network models

In this paper, we have presented a study of how local network topology of spiking neural networks influences functional reconstruction using two model free methods. We used two methods that were based on different ideas of correlation between neurons; spike pat- terns with TE and neuron firing rate with CC. We used the directed correlations produced by each to determine information flow. As CC is a more coarse measure of correlation, we saw a higher number of FP’s in CC functional networks as opposed to TE functional networks. This result implies that some neurons have more influence over neurons with which they do not directly synapse. FN’s occurred in similar numbers in both TE and CC. As FP’s and FN’s can inform us as to how information is processed in a network, we used multiplex networks to identify functional edges of this nature. We defined dyadic and tri- adic transformations to be multilayer subgraphs of our multiplex networks and used null models to determine the statistical significance of transformations. Using dyadic trans- formations, we showed that functional reconstruction for both TE and CC were highly dependent on the structural network and that FP’s are only significant when located between neurons without direct synaptic connection. We also showed that certain struc- tural triadic subgraphs, the chain and hub structures, were likely to produce FP’s and that the direction of the information flow for the FP is determined by the structure. As these structures allow for information flow between neurons that are not connected, they may be biologically beneficial, allowing significant communication between neurons without the cost of synapses.
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Constella: A Complete IP Network Topology Discovery Solution

Constella: A Complete IP Network Topology Discovery Solution

hosts connected by a logical link. Network topology constantly changes as nodes and links join a network, personnel move offices, and network capacity is increased to deal with added traffic. Keeping track of network topology manually is a frustrating and often impossible job. Network topology knowledge including the path between endpoints, can play an important role in analyzing, engineering, and visualizing networks. Most of the previous work [7][9][10][11][13][14] focus on improving the efficiency in terms of time and completeness of network topology discovery algorithms and less attention has been given to the deployment scenarios and user centric view of network topology discovery. The goal of our work is to automatically discover network topology and visualize it. We have been doing research on network topology discovery for the last few years now [1][2][3][5][6] and this paper is summary of our earlier work in order to propose an integrated complete IP network topology solution. In addition to that in the next section we explain the novel objectives of our work.
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Network Topology of People NEWS

Network Topology of People NEWS

Abstract: The new method in a survey on public opinion called People NEWS will be highlighted. The application of NEWS which stand for needs, expectations, wants and satisfaction will be highlighted in the process of air travel in Malaysia. The passenger’s opinions according to their NEWS from the first stage process at the departure airport until the final process at the arrival airport will be discussed based on their network topology. The information from the NEWS network can be filtered by using the minimal spanning tree and the description about the behaviour of the network can be explained by using centrality measures. Some important results and recommendations based on passenger’s NEWS for Malaysian commercial flights will be highlighted.
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