Power Optimization for Low Transmission Delay in Software Defined Data Center Networks

Software-Defined Data Center Networks (SDDCNs) utilizes Software Defined Networking (SDN) as a network architecture to achieve highly flexible, programmable, and automated management of Data Center Networks (DCNs). The high energy consumption of DCNs remains a persistent and significant challenge. Thus, the energy saving is crucial and imperative for DCNs. Current energy-efficient solutions primarily rely on flow consolidation and dynamic device sleeping techniques to reduce energy consumption. However, these approaches often yield long Flow Completion Time (FCT), potentially resulting in violations of service-level agreements, particularly for delay-sensitive applications. In this paper, we formulate the problem of minimizing the power consumption in SDDCNs as key objective while ensuring timely FCT for delay-sensitive applications. To solve this problem, we first introduce the Active Network Generation (ANG) approach, which generates a minimal active subnet with the least number of active devices while meeting the current traffic demand. Subsequently, we propose two algorithms based on the type of applications: the Delay-Tolerant Flow Route (DTFR) algorithm for delay-tolerant applications and the Delay-Sensitive Flow Route (DSFR) algorithm for delay-sensitive applications. Simulation results demonstrate that our propose solution achieves an energy-saving rate of up to 67.77% and significantly reduces FCT compared to benchmark solutions.

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Publication Details

Journal
IEEE Transactions on Mobile Computing
Published
2025-02-07
DOI
https://doi.org/10.1109/tmc.2025.3538791
Citations
2
Primary Topic
Software-Defined Networks and 5G
Type
article
Field-Weighted Citation Impact
2.97
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article

Power Optimization for Low Transmission Delay in Software Defined Data Center Networks

Xiaojie Liu, Qiang He, Fuliang Li, Xingwei Wang et al.
2 citations
IEEE Transactions on Mobile Computing
Software-Defined Networks and 5G
2.97
article

Power Optimization for Low Transmission Delay in Software Defined Data Center Networks

Xiaojie Liu, Qiang He, Fuliang Li, Xingwei Wang, Yong Zhao, Jiannong Cao
article en
2 citations

Abstract

Software-Defined Data Center Networks (SDDCNs) utilizes Software Defined Networking (SDN) as a network architecture to achieve highly flexible, programmable, and automated management of Data Center Networks (DCNs). The high energy consumption of DCNs remains a persistent and significant challenge. Thus, the energy saving is crucial and imperative for DCNs. Current energy-efficient solutions primarily rely on flow consolidation and dynamic device sleeping techniques to reduce energy consumption. However, these approaches often yield long Flow Completion Time (FCT), potentially resulting in violations of service-level agreements, particularly for delay-sensitive applications. In this paper, we formulate the problem of minimizing the power consumption in SDDCNs as key objective while ensuring timely FCT for delay-sensitive applications. To solve this problem, we first introduce the Active Network Generation (ANG) approach, which generates a minimal active subnet with the least number of active devices while meeting the current traffic demand. Subsequently, we propose two algorithms based on the type of applications: the Delay-Tolerant Flow Route (DTFR) algorithm for delay-tolerant applications and the Delay-Sensitive Flow Route (DSFR) algorithm for delay-sensitive applications. Simulation results demonstrate that our propose solution achieves an energy-saving rate of up to 67.77% and significantly reduces FCT compared to benchmark solutions.

IEEE Transactions on Mobile ComputingVol. 24(7)
Hong Kong Polytechnic University (HK), Peng Cheng Laboratory (CN), Cloud Computing Center (CN), Northeastern University (CN)
Affordable and clean energy
Openalex Percentile: Top 10%
Software-Defined Networks and 5G
2.97
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Power Optimization for Low Transmission Delay in Software Defined Data Center Networks — Xiaojie Liu, Qiang He, et al. · IEEE Transactions on Mobile Computing (2025) | TGRS Research Map | TGRS