28,213 research outputs found
DTMsim - DTM channel simulation in ns
Dynamic Transfer Mode (DTM) is a ring based MAN technology that
provides a channel abstraction with a dynamically adjustable capacity.
TCP is a reliable end to end transport protocol capable of adjusting
its rate. The primary goal of this work is investigate the coupling
of dynamically allocating bandwidth to TCP flows with the affect this
has on the congestion control mechanism of TCP. In particular we
wanted to find scenerios where this scheme does not work, where either
all the link capacity is allocated to TCP or congestion collapse
occurs and no capacity is allocated to TCP. We have created a
simulation environment using ns-2 to investigate TCP over networks
which have a variable capacity link. We begin with a single TCP Tahoe
flow over a fixed bandwidth link and progressively add more complexity
to understand the behaviour of dynamically adjusting link capacity to
TCP and vice versa
An Adaptive Fault-Tolerant Communication Scheme for Body Sensor Networks
A high degree of reliability for critical data transmission is required in
body sensor networks (BSNs). However, BSNs are usually vulnerable to channel
impairments due to body fading effect and RF interference, which may
potentially cause data transmission to be unreliable. In this paper, an
adaptive and flexible fault-tolerant communication scheme for BSNs, namely
AFTCS, is proposed. AFTCS adopts a channel bandwidth reservation strategy to
provide reliable data transmission when channel impairments occur. In order to
fulfill the reliability requirements of critical sensors, fault-tolerant
priority and queue are employed to adaptively adjust the channel bandwidth
allocation. Simulation results show that AFTCS can alleviate the effect of
channel impairments, while yielding lower packet loss rate and latency for
critical sensors at runtime.Comment: 10 figures, 19 page
Simulation of the Effect of Data Exchange Mode Analysis on Network Throughput
Emergence of large scale specialized networks with a large number of computers
marked a new stage in network infrastructure development. In connection with the wide
variety of modern network equipment used for construction of large-scale networks and
increasing complexity of such networks and applications, a developer or system
administrator can no longer depend mainly on intuitive decisions. Optimum network configuration for realization of concrete tasks and effective deployment of applications in modern terms is not possible without conducting a proper research with the use of specialized simulation tools. Increase in bandwidth is followed by a commensurate increase
in the amount of traffic sent over the Internet. Optimizing the use and allocation of bandwidth continues to be an ongoing problem. We present a simulation model to resolve
the technological challenges of increasing the efficiency of data exchange in computer networks
A variational approach for continuous supply chain networks
We consider a continuous supply chain network consisting of buffering queues and processors first proposed by [D. Armbruster, P. Degond, and C. Ringhofer, SIAM J. Appl. Math., 66 (2006), pp. 896–920] and subsequently analyzed by [D. Armbruster, P. Degond, and C. Ringhofer, Bull. Inst. Math. Acad. Sin. (N.S.), 2 (2007), pp. 433–460] and [D. Armbruster, C. De Beer, M. Fre- itag, T. Jagalski, and C. Ringhofer, Phys. A, 363 (2006), pp. 104–114]. A model was proposed for such a network by [S. G ̈ottlich, M. Herty, and A. Klar, Commun. Math. Sci., 3 (2005), pp. 545–559] using a system of coupling ordinary differential equations and partial differential equations. In this article, we propose an alternative approach based on a variational method to formulate the network dynamics. We also derive, based on the variational method, a computational algorithm that guarantees numerical stability, allows for rigorous error estimates, and facilitates efficient computations. A class of network flow optimization problems are formulated as mixed integer programs (MIPs). The proposed numerical algorithm and the corresponding MIP are compared theoretically and numerically with existing ones [A. Fu ̈genschuh, S. Go ̈ttlich, M. Herty, A. Klar, and A. Martin, SIAM J. Sci. Comput., 30 (2008), pp. 1490–1507; S. Go ̈ttlich, M. Herty, and A. Klar, Commun. Math. Sci., 3 (2005), pp. 545–559], which demonstrates the modeling and computational advantages of the variational approach
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