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Sustainable Virtualization Techniques for Reducing Carbon Footprint in Modern IT Infrastructures

現代のITインフラにおけるカーボンフットプリント削減のための持続可能な仮想化技術 (AI 翻訳)

Alfaiz Phatan, KS Atram, Pankah Jha

Best Journal of Innovation in Science, Research and Development📚 査読済 / ジャーナル2026-07-01#省エネ経営インパクト: コスト削減対象セクター: cross_sector
DOI: 10.67249/739053
原典: https://doi.org/10.67249/739053

🤖 gxceed AI 要約

日本語

本論文は、クラウドコンピューティングやAI、IoTなどの急速な発展に伴い増大するITインフラのエネルギー消費と二酸化炭素排出に対処するため、サーバ仮想化、ストレージ仮想化、ネットワーク仮想化、コンテナ化といった持続可能な仮想化技術を包括的に調査する。特に、サーバ統合、VMマイグレーション、エネルギー認識スケジューリング、動的リソース割り当て、電力管理などの省エネルギー技術に焦点を当て、それらの利点と課題を議論する。また、AIベースのリソース割り当てや再生可能エネルギー利用、エッジコンピューティング、カーボンアウェアコンピューティングなど将来のトレンドも展望する。

English

This paper surveys sustainable virtualization technologies (server, storage, network virtualization, and containerization) to reduce carbon footprint in modern IT infrastructures. It covers energy-saving techniques such as server consolidation, VM migration, energy-aware scheduling, dynamic resource allocation, and power management, discussing benefits and challenges. Future trends including AI-based resource allocation, renewable energy, edge computing, and carbon-aware architectures are also explored.

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

📝 gxceed 編集解説 — Why this matters

日本のGX文脈において

日本ではデータセンターのエネルギー消費が増加しており、本論文で紹介される仮想化技術は、省エネルギーとコスト削減に寄与する。日本のGX政策やデータセンターのグリーン化に向けた取り組みに活用できる知見を提供する。

In the global GX context

Globally, data center energy efficiency is critical for IT sector decarbonization. This survey provides a practical overview of virtualization techniques that can be integrated into corporate sustainability strategies, relevant to TCFD/ISSB disclosures on energy management and Scope 2 emissions.

👥 読者別の含意

🔬研究者:Provides a broad survey of virtualization techniques for energy efficiency, useful as a starting point for further research.

🏢実務担当者:Offers actionable insights into implementing server consolidation, VM migration, and power management to reduce energy costs and carbon footprint.

🏛政策担当者:Highlights the role of virtualization in energy-efficient IT infrastructure, supporting policy development for green computing standards.

📄 Abstract(原文)

: Rapid development of cloud computing, artificial intelligence, Internet of Things (IoT), and big data analysis has made modern IT infrastructures consume excessive amounts of energy. Data centers all over the globe use huge amounts of electric power to perform computational tasks and cause higher costs and more carbon dioxide emissions. Thus, sustainable computing has become a necessary method for saving the environment and achieving maximum performance levels at the same time. Nowadays, one of the most advanced technologies which help achieve sustainable IT is virtualization because several virtual systems can be deployed on the same hardware system [1][2]. In this paper, the focus will be on sustainable virtualization technologies that help reduce carbon footprint of modern IT infrastructures. Server virtualization, storage virtualization, network virtualization, and containerization technologies will be described in detail. Besides, this research will provide information about sustainability technologies such as server consolidation, VM migration, energy-aware scheduling, dynamic resource allocation, and power management [3][4]. Moreover, this paper discusses some advantages of virtualization in terms of energy savings, lower expenses, scalability, flexibility, and enhanced disaster recovery processes. Performance overhead, security, management difficulties, and migration expenses are discussed as well [5][6]. Also, this paper discusses future trends in virtualization technology, which include AI-based allocation of resources, the use of renewable energy sources, edge computing, and carbon-aware computing architectures. This paper argues that virtualization technology is crucial for creating green sustainable computing infrastructure capable of meeting future computational needs [7][8].

🔗 Provenance — このレコードを発見したソース

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gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。