Modeling of Data Communication Networks using Dynamic Complex Networks and its Performance Studies

Kumari, Suchi; Singh, Anurag

doi:10.1007/978-3-319-50901-3_3

Cited by 5 publications

(2 citation statements)

References 37 publications

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“…Links are divided into three categories: newly added, rewired and removed links. Distribution of the links are done using the given set of rules [29].…”

Section: Proposed Workmentioning

confidence: 99%

Fair end‐to‐end window‐based congestion control in time‐varying data communication networks

Kumari

Singh

2019

Int J Communication

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Communication networks are time-varying and hence, fair sharing of network resources among the users in such dynamic environment is a challenging task. In this context, a time-varying network model is designed and shortest user's route is found. In the designed network model, an end to end window-based congestion control scheme is developed with the help of internal nodes or router and the end user can get implicit feedback (RTT and throughput). This scheme is considered as fair if the allocation of resources among users minimizes overall congestion or backlog in the networks. Window update approach is based on multi-class fluid model and is updated dynamically by considering delays (communication, propagation and queuing) and the backlog of packets in the user's routes. Convergence and stability of the window size are obtained using a Lyapunov function. A comparative study with other window-based methods is also provided. IntroductionReal life systems such as communication, social, biological can be described with the help of complex networks [1,2]. It is a challenging task to design a time-varying communication network (TVCN) with the ability to respond to the randomly changing traffic. Hence, the study of the traffic dynamics and fair sharing of resources on communication networks has received a great wave of interest for the researchers in past few years. The allocation of resources among the users in an unbiased manner is one of the challenging tasks in today's scenario. For assigning resources in an unbiased or fair manner, some researchers use various rate vector allocation schemes [3,4] to gain maximum utility while others select fair end to end window-based congestion control scheme [5,4]. In this paper, we are interested to design a TVCN model considering network growth, redistribution of traffic from heavily loaded nodes and removal of some fraction of links to reduce maintenance cost. A window-based congestion control scheme may be applied on the proposed TVCN model and user's current window size may be updated by considering delays(communication, propagation and queuing delays).Communication networks are evolving and the concept of evolving networks with preferential linking during the addition of new nodes is introduced by Barabasi-Albert ([6]). The distribution of degrees of nodes in these networks follow the power law and is termed as scale free nature of the networks. Many time-varying graph (TVG) models are proposed [7,8]. A series of static graphs (i.e., the snapshots) is used to represent the network at a given time instant [9]. Wehmuth et al. [7] proposed a new unifying model for representing finite discrete TVGs. A framework is designed to obtain degree distribution of evolving network with the consideration of deletion of nodes and a continuum formulation is also provided by [10]. A preferential attachment model for network growth is proposed where a new node has partial information about the network [11]. A new node has access to a fraction of nodes and a new connection is formed with the...

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“…Links are divided into three categories: newly added, rewired and removed links. Distribution of the links are done using the given set of rules [29].…”

Section: Proposed Workmentioning

confidence: 99%

Fair end‐to‐end window‐based congestion control in time‐varying data communication networks

Kumari

Singh

2019

Int J Communication

Self Cite

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“…This system is considered as a complex network, and the units that compose the entire system is regarded as the nodes of the complex network. The introduction of a complex network model provides a huge convenience for people to solve many practical problems and has been extensively investigated [1][2][3]. Synchronization, as a major feature of complex networks, has attracted considerable attention.…”

Section: Introductionmentioning

confidence: 99%

Finite-time synchronization of uncertain complex dynamic networks with time-varying delay

Luo

Yao

2020

Adv Differ Equ

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This study investigates the finite-time synchronization of uncertain nonlinear complex dynamic networks with time-varying delay. For a class of complex network models with time-varying delay and uncertain system parameters, the time delay changes infrequently, uncertain terms are unknown but bounded, and the matching conditions are satisfied. The coupling relationship between nodes is a nonlinear function with time delay, and the function satisfies the Lipschitz condition. A new criterion for the finite-time synchronization of a class of complex dynamical networks with variable delay is obtained, and the upper bound of the time for the system to achieve synchronization is presented by constructing a suitable Lyapunov-Krasovskii function, designing a nonlinear controller, and combining analysis techniques, such as matrix inequality. Finally, the validity of finite-time synchronization is verified through computer simulation.

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Optimal Local Routing Strategies for Community Structured Time Varying Communication Networks

Kumari¹,

Singh²,

Cherifi³

2017

Lecture Notes in Computer Science

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Modeling of Data Communication Networks using Dynamic Complex Networks and its Performance Studies

Cited by 5 publications

References 37 publications

Fair end‐to‐end window‐based congestion control in time‐varying data communication networks

Fair end‐to‐end window‐based congestion control in time‐varying data communication networks

Finite-time synchronization of uncertain complex dynamic networks with time-varying delay

Optimal Local Routing Strategies for Community Structured Time Varying Communication Networks

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