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AIMD in a discrete time implementation or with a non-constant shared resource

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conference contribution
posted on 2025-05-08, 17:50 authored by S. Stüdli, Richard MiddletonRichard Middleton, Julio BraslavskyJulio Braslavsky, R. Shorten
The additive increase multiplicative decrease (AIMD) algorithm, that is commonly used for congestion avoidance in communication networks, has recently been suggested in other fields such as load management in electric power networks. As for congestion avoidance, in such systems a large number of agents are required to share a given resource. In recent work by Shorten, Wirth and Leith on congestion control in networking a stochastic model has been developed to analyse AIMD algorithms. However, the analysis assumes a continuous implementation of the algorithm and a constant available resource. These assumptions are no longer useful if the AIMD algorithm is applied in fields such as load management in electric power networks, where a discrete implementation is often required, and the available resource shared may be inherently variable. In this paper we develop a disturbed AIMD model based on the model introduced by Shorten et al. that includes discrete time implementation and time varying resource availability. Further, we use that model to bound the influence of these disturbances, caused by either a discrete implementation or small variations in the available resource.

History

Source title

Proceedings of the 2015 5th Australian Control Conference

Name of conference

2015 5th Australian Control Conference (AUCC)

Location

Gold Coast, Qld

Start date

2015-11-05

End date

2015-11-06

Pagination

230-235

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Place published

Piscataway

Language

  • en, English

College/Research Centre

Faculty of Engineering and Built Environment

School

School of Electrical Engineering and Computer Science

Rights statement

© 2015 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.

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