Tareg95/Modeling-of-the-Energy-Consumption-of-AI-Inference-Workloads
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SUMMARY AI summary by gpt-5-mini
このリポジトリは、AI推論を処理するGPUクラスタの24時間分の消費電力とCO₂排出をシミュレーションするモデルです。研究者や運用エンジニアが、ワークロード特性やプロビジョニング方針がサービス品質と環境負荷に与える影響を定量評価するために使います。主な機能は、NHPP（昼夜変動）にParetoバーストを重ねた到着モデル、Static／Conservative／Aggressiveのプロビジョニング戦略、15分単位のコールドスタート遅延、GPUの3状態（Active／Execution‑idle／Deep‑idle）による消費電力モデル、動作別のエネルギー内訳算出、国別の電力炭素強度を用いたCO₂計算です。Jupyterノートブックで順に実行し、6通りの戦略×到着パターン比較表を出力します。必要なライブラリと再現手順もREADMEに記載されています。
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i´m a computer engineer student at KTH
Dates
| Created on GitHub | 2026-05-09 |
| Last push | 2026-05-09 |
| First seen here | 2026-05-09 |
| Last fetched | 2026-05-09 14:57 |
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