International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Volume 7 Issue 6
November-December 2025

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A Comprehensive Architecture for Reducing Power Consumption in Modern Cloud Infrastructures

Author(s) Dr. SURENDER SINGH
Country India
Abstract The explosive growth of cloud computing, AI workloads, and hyper scale data centers has generated unprecedented levels of network traffic and energy consumption. Current estimates indicate that data centers account for more than 1% of global electricity usage, with projections to double by 2030 as AI-driven applications surge. This paper presents a systematic analysis and a novel architecture for improving energy efficiency within data center networks (DCNs). We evaluate limitations of traditional architectures, including high-capacity electronic switches, always-on routing paths, inefficient traffic spreading, and the absence of dynamic power management. To address these challenges, we propose an Energy-Aware Adaptive Networking Architecture (EAANA) that integrates: (i) traffic-aware link scaling, (ii) predictive machine-learning-based flow scheduling, (iii) dynamic optical-electronic hybrid switching, and (iv) renewable-source-aware routing. Experimental evaluation using simulated hyper scale workloads shows that EAANA reduces network energy consumption by up to 37% without compromising throughput or latency. The proposed model demonstrates a scalable and practical pathway for greener next-generation data centers.
Keywords Energy efficiency, data center networks, green networking, flow scheduling, optical switching, cloud computing, machine learning, power optimization.
Field Computer > Network / Security
Published In Volume 7, Issue 6, November-December 2025
Published On 2025-12-03
DOI https://doi.org/10.36948/ijfmr.2025.v07i06.62237
Short DOI https://doi.org/hbdsj3
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