Adaptive sampling technique for computer network traffic parameters using a combination of fuzzy system and regression model

SALAMA, A, SAATCHI, Reza and BURKE, Derek (2018). Adaptive sampling technique for computer network traffic parameters using a combination of fuzzy system and regression model. In: 2017 4th International Conference on Mathematics and Computers in Science in Industry. MCSI 2017. Island, Greece, August 24-26, 2017. IEEE, 206-211.

[img]
Preview
PDF
Saatchi - Adaptive sampling technitue for computer network traffic parameters using a combination of fuzzy system (AM).pdf - Accepted Version
All rights reserved.

Download (895kB) | Preview
Official URL: https://ieeexplore.ieee.org/document/8326842
Link to published version:: https://doi.org/10.1109/MCSI.2017.43
Related URLs:

Abstract

In order to evaluate the effectiveness of wired and wireless networks for multimedia communication, suitable mechanisms to analyse their traffic are needed. Sampling is one such mechanism that allows a subset of packets that accurately represents the overall traffic to be formed thus reducing the processing resources and time. In adaptive sampling, unlike fixed rate sampling, the sample rate changes in accordance with transmission rate or traffic behaviour and thus can be more optimal. In this study an adaptive sampling technique that combines regression modelling and a fuzzy inference system has been developed. It adjusts the sampling according to the variations in the traffic characteristics. The method's operation was assessed using a computer network simulated in the NS-2 package. The adaptive sampling evaluated against a number of non-adaptive sampling gave an improved performance.

Item Type: Book Section
Additional Information: © 2018 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords: adaptive sampling; computer network quality of service; regression model; fuzzy logic.
Research Institute, Centre or Group - Does NOT include content added after October 2018: Materials and Engineering Research Institute > Engineering Research
Identification Number: https://doi.org/10.1109/MCSI.2017.43
Page Range: 206-211
Depositing User: Reza Saatchi
Date Deposited: 20 Jul 2017 14:51
Last Modified: 24 Feb 2020 09:52
URI: http://shura.shu.ac.uk/id/eprint/15933

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics