An Adaptive Routing Framework for Efficient Power Consumption in Software-Defined Datacenter Networks
Data Center Networks (DCNs) form the backbone of many Internet applications and services that have become necessary in daily life. Energy consumption causes both economic and environmental issues. It is reported that 10% of global energy consumption is due to ICT and network usage. Computer networki...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/10/23/3027 |
| _version_ | 1797507951705980928 |
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| author | Mohammed Nsaif Gergely Kovásznai Anett Rácz Ali Malik Ruairí de Fréin |
| author_facet | Mohammed Nsaif Gergely Kovásznai Anett Rácz Ali Malik Ruairí de Fréin |
| author_sort | Mohammed Nsaif |
| collection | DOAJ |
| description | Data Center Networks (DCNs) form the backbone of many Internet applications and services that have become necessary in daily life. Energy consumption causes both economic and environmental issues. It is reported that 10% of global energy consumption is due to ICT and network usage. Computer networking equipment is designed to accommodate network traffic; however, the level of use of the equipment is not necessarily proportional to the power consumed by it. For example, DCNs do not always run at full capacity yet the fact that they are supporting a lighter load is not mirrored by a reduction in energy consumption. DCNs have been shown to unnecessarily over-consume energy when they are not fully loaded. In this paper, we propose a new framework that reduces power consumption in software-defined DCNs. The proposed approach is composed of a new Integer Programming model and a heuristic link utility-based algorithm that strikes a balance between energy consumption and performance. We evaluate the proposed framework using an experimental platform, which consists of an optimization tool called LinGo for solving convex and non-convex optimization problems, the POX controller and the Mininet network emulator. Compared with the state-of-the-art approach, the equal cost multi-path algorithm, the results show that the proposed method reduces the power consumption by up to 10% when the network is experiencing a high traffic load and 63.3% when the traffic load is low. Based on these results, we outline how machine learning approaches could be used to further improve our approach in future work. |
| first_indexed | 2024-03-10T04:55:42Z |
| format | Article |
| id | doaj.art-d92e8a31997e478bb6e404989a49c144 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T04:55:42Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-d92e8a31997e478bb6e404989a49c1442023-11-23T02:17:56ZengMDPI AGElectronics2079-92922021-12-011023302710.3390/electronics10233027An Adaptive Routing Framework for Efficient Power Consumption in Software-Defined Datacenter NetworksMohammed Nsaif0Gergely Kovásznai1Anett Rácz2Ali Malik3Ruairí de Fréin4Department of Information Technology, University of Debrecen, 4032 Debrecen, HungaryDepartment of Computational Science, Eszterhazy Karoly Catholic University, 3300 Eger, HungaryDepartment of Applied Mathematics & Probability Theory, University of Debrecen, 4032 Debrecen, HungarySchool of Electrical and Electronic Engineering, Technological University Dublin, D07 EWV4 Dublin, IrelandSchool of Electrical and Electronic Engineering, Technological University Dublin, D07 EWV4 Dublin, IrelandData Center Networks (DCNs) form the backbone of many Internet applications and services that have become necessary in daily life. Energy consumption causes both economic and environmental issues. It is reported that 10% of global energy consumption is due to ICT and network usage. Computer networking equipment is designed to accommodate network traffic; however, the level of use of the equipment is not necessarily proportional to the power consumed by it. For example, DCNs do not always run at full capacity yet the fact that they are supporting a lighter load is not mirrored by a reduction in energy consumption. DCNs have been shown to unnecessarily over-consume energy when they are not fully loaded. In this paper, we propose a new framework that reduces power consumption in software-defined DCNs. The proposed approach is composed of a new Integer Programming model and a heuristic link utility-based algorithm that strikes a balance between energy consumption and performance. We evaluate the proposed framework using an experimental platform, which consists of an optimization tool called LinGo for solving convex and non-convex optimization problems, the POX controller and the Mininet network emulator. Compared with the state-of-the-art approach, the equal cost multi-path algorithm, the results show that the proposed method reduces the power consumption by up to 10% when the network is experiencing a high traffic load and 63.3% when the traffic load is low. Based on these results, we outline how machine learning approaches could be used to further improve our approach in future work.https://www.mdpi.com/2079-9292/10/23/3027DCNinteger programmingoptimizationpower consumptionQoSSDN |
| spellingShingle | Mohammed Nsaif Gergely Kovásznai Anett Rácz Ali Malik Ruairí de Fréin An Adaptive Routing Framework for Efficient Power Consumption in Software-Defined Datacenter Networks Electronics DCN integer programming optimization power consumption QoS SDN |
| title | An Adaptive Routing Framework for Efficient Power Consumption in Software-Defined Datacenter Networks |
| title_full | An Adaptive Routing Framework for Efficient Power Consumption in Software-Defined Datacenter Networks |
| title_fullStr | An Adaptive Routing Framework for Efficient Power Consumption in Software-Defined Datacenter Networks |
| title_full_unstemmed | An Adaptive Routing Framework for Efficient Power Consumption in Software-Defined Datacenter Networks |
| title_short | An Adaptive Routing Framework for Efficient Power Consumption in Software-Defined Datacenter Networks |
| title_sort | adaptive routing framework for efficient power consumption in software defined datacenter networks |
| topic | DCN integer programming optimization power consumption QoS SDN |
| url | https://www.mdpi.com/2079-9292/10/23/3027 |
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