強化学習を用いたエッジIoTにおける遅延シフトによる分散型キュー制御
1Vinnytsia National Technical University, Vinnytsia, Ukraine. kovtun_v_v@vntu.edu.ua.
Scientific reports
|August 22, 2025
まとめ
この研究は,要求サービスを効率的に管理するためのエッジIoTシステムの適応モデルを導入します. 処理時間を動的に調整することで,不安定なトラフィックでもサービスの品質 (QoS) とエネルギー効率を改善します.
科学分野:
- コンピュータ科学
- 電気工学
- 応用数学
背景:
- エッジIoTシステムは,エネルギー効率,応答性,および自己規制に対する要求が増加する課題に直面しています.
- エッジネットワークの不安定なトラフィック条件は,適応的なサービス管理戦略を必要とします.
- 既存のモデルには,ダイナミックなQoSとエネルギー管理の要件に対応する柔軟性が欠けていることが多い.
研究 の 目的:
- エッジIoTシステムの周辺ノードでのリクエストサービスプロセスのモデリングと管理のための適応的なアプローチを開発する.
- 変動する交通条件下でのエネルギー効率,応答性,および自己規制を強化する.
- ダイナミックなQoSとエネルギー管理のためのスケーラブルでトラフィック型アグノスティックなソリューションを提供する.
主な方法:
- デバイスの不可用性を考慮するために,パラメータ化された時間シフトを持つストキャスティックG/G/1モデルが提案されました.
- サービス品質 (QoS) の指標 (遅延,変動性,損失,エネルギー消費) の分析表現は,シフトパラメータの関数として導出されました.
- ディープQネットワーク (DQN) ベースの補強学習エージェントは,シフトパラメータの分散型,リアルタイム制御のために実装されました.
主要な成果:
- 最先端のモデルと比較して平均遅延が17~26%減少した.
- サービス時間の変動が減少し,ピークロード後の列の回復の安定性が向上しました.
- 提案されたソリューションはトラフィックタイプ無関係で,多様なエッジアーキテクチャにわたってスケーラブルです.
結論:
- 適応的なアプローチは,エッジIoTシステムのサービスプロセスを効果的にモデル化し管理し,QoSとエネルギー効率を向上させます.
- DQNベースのエージェントは,ダイナミックで分散した制御を提供し,リアルタイムのキュー状態に適応します.
- この結果は,センサーネットワーク,5G/6Gエッジシナリオ,ダイナミックなQoSとエネルギー管理を必要とするシステムに適用できます.
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