Demand-Driven Server Deployment for Green Computing Power Networks: A Multi-Objective Hierarchical Optimization Approach
グリーンコンピューティングパワーネットワークのための需要駆動型サーバー配置：多目的階層的最適化アプローチ (AI 翻訳)
Wen Wen, Renchao Xie, Qinqin Tang, Zehui Xiong, Ran Zhang, Gaochang Xie, Siqi Sun, Tao Huang
🤖 gxceed AI 要約
日本語
本研究は、コンピューティングパワーネットワーク(CPN)において、再生可能エネルギーの変動や電気料金、タスク特性を考慮した需要駆動型サーバー配置手法を提案する。時空間タスクスケジューリングと多目的進化アルゴリズム(MOEA)を組み合わせた階層的解法により、二酸化炭素排出量と年間コストを削減し、グリーンエネルギー利用率を向上させることを実証した。
English
This paper proposes a demand-driven server deployment scheme for Computing Power Networks (CPNs) that accounts for spatiotemporal variations in renewable energy availability, electricity prices, and task characteristics. A hierarchical solution using a Multi-Objective Evolutionary Algorithm (MOEA) optimizes task scheduling and server deployment iteratively. Simulation results show significant reductions in carbon emissions and annual costs while increasing green energy usage.
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増加が課題となっており、本手法は再生可能エネルギーを活用した効率的なサーバー配置に寄与する。GX実現に向けたコンピューティングインフラのグリーン化に示唆を与える。
In the global GX context
As global data center energy demand surges, this optimization framework offers a practical approach to integrate renewable energy and reduce carbon footprints. It aligns with the growing emphasis on energy efficiency and sustainability in cloud and edge computing infrastructure.
👥 読者別の含意
🔬研究者:Provides a novel multi-objective optimization framework for green computing power networks that integrates demand response and hierarchical decision-making.
🏢実務担当者:Offers a method for data center operators to reduce costs and carbon emissions by dynamically deploying servers based on renewable energy and pricing signals.
🏛政策担当者:Highlights the potential of demand-driven scheduling and renewable integration in computing infrastructure, informing energy policy for digital sustainability.
📄 Abstract（原文）
Computing Power Networks (CPNs) have become an essential network architecture for supporting emerging applications, where an efficient server deployment scheme is critical to meeting growing demands. However, existing studies on server deployment schemes overlook the significant spatiotemporal variations in renewable energy availability and electricity prices, and inadequately consider network paths and task characteristics, ultimately leading to suboptimal decisions. To bridge this gap, this paper proposes a demand-driven server deployment scheme enabled by a spatiotemporal task scheduling strategy. Firstly, for the task scheduling scheme, we leverage a demand response program to perform triple selection of computing resources, routing paths, and forwarding time. Then, for the server deployment scheme, we obtain the computing resource demand of each CPN node based on the optimized scheduling decisions and further select energy-efficient servers with low procurement costs. Due to the interdependence between the scheduling and deployment schemes, a Multi-Objective Evolutionary Algorithm (MOEA)-based hierarchical solution is developed to iteratively find the optimal deployment solution. Simulation results show that the proposed scheme significantly reduces carbon emissions and annual costs while increasing the proportion of green energy usage, outperforming benchmark methods. The findings demonstrate the effectiveness of integrating scheduling and deployment for building efficient and sustainable CPN infrastructures.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1109/tgcn.2026.3653570first seen 2026-05-15 17:35:36
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