Carbon-Aware VM Placement via Surrogate-Guided Adaptive Swarm Optimization in Green Cloud Data Centers
カーボンアウェアVM配置：サロゲート誘導適応型群最適化を用いたグリーンクラウドデータセンター (AI 翻訳)
Thi-Kien Dao, Trong-The Nguyen
🤖 gxceed AI 要約
日本語
本論文は、クラウドデータセンターのVM配置において炭素強度信号を考慮した多目的最適化フレームワークCASOを提案。適応型RBFサロゲートモデルと自己適応型PSO-DEハイブリッド最適化を統合し、炭素排出量、エネルギー消費、SLA違反率、ネットワークレイテンシを同時に最小化。Alibaba Cluster Traceデータセットを用いた実験で、炭素排出量を最大31.4%削減し、収束速度も3.8倍向上した。
English
This paper proposes CASO, a framework for carbon-aware VM placement integrating adaptive RBF surrogate model with self-adaptive PSO-DE swarm optimizer. It minimizes carbon emissions, energy, SLA violations, and latency simultaneously under QoS constraints. Experiments on Alibaba trace show 31.4% carbon reduction, 27.9% energy savings, and 3.8× faster convergence than baselines.
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
日本でもデータセンターのエネルギー消費が増加する中、炭素強度に基づくVM配置はSSBJやカーボンプライシングの文脈で重要。CASOはリアルタイムの排出係数に応じた最適化を可能にし、日本企業のScope2削減に貢献する可能性がある。
In the global GX context
As global cloud emissions rise, real-time carbon-aware VM placement ties into ISSB Scope 2 reporting and TCFD metrics. CASO's surrogate-guided optimization offers a scalable solution for multi-objective green data center management.
👥 読者別の含意
🔬研究者:Provides a novel multi-objective optimization framework for carbon-aware resource allocation with three key innovations: online surrogate update, carbon weighting, adaptive parameter control.
🏢実務担当者:Data center operators can adopt CASO to reduce carbon footprint and energy costs while maintaining SLA performance; the framework is validated on real Alibaba trace.
🏛政策担当者:Demonstrates feasibility of integrating real-time grid emission factors into data center operations, supporting carbon accounting and reporting under emerging disclosure standards.
📄 Abstract（原文）
The rapid proliferation of cloud data centers has intensified concerns over carbon emissions, energy efficiency, and sustainability. Virtual machine (VM) placement is a pivotal control lever, yet existing methods rarely couple carbon intensity signals with computationally tractable multi-objective optimization. In this paper, we propose CASO (Carbon-Aware Surrogate-Guided Optimization), a novel framework that integrates an online adaptive Radial Basis Function (RBF) surrogate model with a self-adaptive hybrid PSO-DE swarm optimizer for real-time VM placement in geo-distributed edge cloud environments. CASO simultaneously minimizes carbon emissions, energy consumption, SLA violation rate, and network latency under strict host capacity and Quality-of-Service (QoS) constraints. Three key innovations differentiate CASO: (i) an online surrogate update mechanism that refines fitness approximations incrementally as workload patterns evolve; (ii) a carbon intensity weighting scheme anchored to real-time Grid Emission Factor (GEF) signals; and (iii) an adaptive parameter controller that autonomously tunes swarm exploration–exploitation trade-offs without hand-crafting. Experiments on the publicly available Alibaba Cluster Trace (cluster-trace-v2026-GenAI) dataset within a CloudSim-Plus environment show that CASO reduces carbon emissions by up to 31.4%, energy consumption by 27.9%, and SLA violations by 18.8% compared to the strongest baseline while converging 3.8× faster than the strongest baseline (ADEDL).
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/su18126092first seen 2026-06-19 05:19:02
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