Impact of source load and routing on QoS of packets delivery

Ławniczak, Anna T.; Xie, Shengkun

doi:10.1016/j.jocs.2010.03.013

Cited by 5 publications

(3 citation statements)

References 18 publications

(39 reference statements)

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“…An energy effi cient distributed unequal clustering algorithm which provides a new way of creating distributed clusters has been found to solve the hot spot problem and increase the network lifetime 12 . The effi ciency of performance of a change in routing to accommodate the volume of the incoming traffi c has been investigated 13 . A re-clustering strategy and a redirection scheme has been presented for cluster based WSNs in order to address the power conserving issues in addition to retaining the merits of the clustering approach 14 .…”

Section: Related Workmentioning

confidence: 99%

Cluster Based Data Transfer Scheme for Virtual Private Networks

Mahalakshmi

Ramaswamy

2012

IET Chennai 3rd International Conference on Sustainable Energy and Intelligent Systems (SEISCON 2012)

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The Virtual Private Networks (VPNs) embody themselves as a wireless media, with a view to promote data transfer around a communication network. The VPN endpoints in the network specify bounds on the amount of traffi c that it is likely to transfer at any time. The service provider is required to enable the VPN to guarantee the required bandwidth to cater to the incessant traffi c. The philosophy in this paper augurs to establish a routing path for VPNs, in order to optimize the use of available bandwidth. The network is arranged to be in the form of clusters, with a view to create a mechanism for effective transfer of data within the network. It echoes the use of a Cluster based Adhoc on Demand (CAODV) routing methodology to extract an improved performance for the chosen topology. The NS2 based simulation results evaluated in terms of its metrics portray the suitability of the proposed approach for practical applications.

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Section: Related Workmentioning

confidence: 99%

Cluster Based Data Transfer Scheme for Virtual Private Networks

Mahalakshmi

Ramaswamy

2012

IET Chennai 3rd International Conference on Sustainable Energy and Intelligent Systems (SEISCON 2012)

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“…Another very important "real-time" network performance indicator is an indicator called number of packets in transit (NPT) ( [12,15,16]). This indicator, N v (ecf, λ, k), for a given PSN model with L p (16, ecf, λ) setup and v value of the seed of the first pseudorandom number generator, is given by the total number of packets in the network at time k, that is, by the sum over all network nodes of the number of packets in each outgoing queue at time k. The NPT time series, that is, where ecf = ONE, QS, QSPO, respectively, and λ belongs to FreeFlow set we run simulations with 24 different seed values v, where v = 1, .…”

Section: Experimental Setups Of Psn Model Andmentioning

confidence: 99%

“…The behaviors of the time variability of NPT data are the key characteristics of NPT data ( [15,16]). The time variability of the NPT data shifted by its time average, is denoted by…”

Section: Simulated Npt Datamentioning

confidence: 99%

Study of Stationary Load Increase of Computer-Network Traffic via Dynamic Principal-Component Analysis

Xie

Ławniczak

2012

ISRN Computational Mathematics

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Many network monitoring applications and performance analysis tools are based on the study of an aggregate measure of network traffic, for example, number of packets in transit (NPT). The simulation modeling and analysis of this type of performance indicator enables a theoretical investigation of the underlying complex system through different combination of network setups such as routing algorithms, network source loads or network topologies. To detect stationary increase of network source load, we propose a dynamic principal component analysis (PCA) method, first to extract data features and then to detect a stationary load increase. The proposed detection schemes are based on either the major or the minor principal components of network traffic data. To demonstrate the applications of the proposed method, we first applied them to some synthetic data and then to network traffic data simulated from the packet switching network (PSN) model. The proposed detection schemes, based on dynamic PCA, show enhanced performance in detecting an increase of network load for the simulated network traffic data. These results show usefulness of a new feature extraction method based on dynamic PCA that creates additional feature variables for event detection in a univariate time series.

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