GREEN NETWORKING WITH PACKET PROCESSING ENGINES: MODELING AND OPTIMIZATION
The process of choosing energy-efficient networking tools and devices while limiting resource use is known as green networking. Which pipeline best captures the performance and behaviour of an energy-aware network gadget with Ability to use LPI plus AR? The major objective is to manage both pipeline...
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Published in | NeuroQuantology Vol. 19; no. 5; p. 308 |
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Main Author | |
Format | Journal Article |
Language | English |
Published |
Bornova Izmir
NeuroQuantology
24.05.2023
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Subjects | |
Online Access | Get full text |
ISSN | 1303-5150 |
DOI | 10.48047/nq.2021.19.5.NQ21077 |
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Summary: | The process of choosing energy-efficient networking tools and devices while limiting resource use is known as green networking. Which pipeline best captures the performance and behaviour of an energy-aware network gadget with Ability to use LPI plus AR? The major objective is to manage both pipeline power setup and optimal method of traffic flow distribution. Modern packet processing engines, which frequently consist of a variety of concurrent pipelines to "divide and conquer" the amount of incoming traffic, were the subject of this work. This work focuses on energy-aware gadgets that can lower their energy needs by adjusting their performance. In order to do so properly depict the effects of technologies for green networks (such reduced power idle as well as adaptive capacity) on network- and energy-aware performance measures, it suggests an analytical model. The model has been tested by applying real-world traffic traces and energy-conscious software routers, with positive results. In order to dynamically modify the design of energy-aware devices to reduce energy consumption while managing incoming traffic volumes along with achieving network performance requirements, an optimization process predicated upon the framework has been presented also then put to the test in experiments. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
ISSN: | 1303-5150 |
DOI: | 10.48047/nq.2021.19.5.NQ21077 |