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<p>Modern cloud computing systems require intelligent resource allocation strategies that balance quality-of-service (QoS), operational costs, and energy sustainability. Existing deep Q-learning (DQN) methods suffer from sample inefficiency, centralization bottlenecks, and reactive decision-making during workload spikes. Transformer-based forecasting models such as Temporal Fusion Transformer (TFT) offer improved accuracy but introduce computational overhead, limiting real-time deployment. We propose LSTM-MARL-Ape-X, a novel framework integrating bidirectional Long Short-Term Memory (BiLSTM) for workload forecasting with Multi-Agent Reinforcement Learning (MARL) in a distributed Ape-X architecture. This approach enables proactive, decentralized, and scalable resource management through three innovations: high-accuracy forecasting using BiLSTM with feature-wise attention, variance-regularized credit assignment for stable multi-agent coordination, and faster convergence via adaptive prioritized replay. Experimental validation on real-world traces demonstrates 94.6% SLA compliance, 22% reduction in energy consumption, and linear scalability to over 5,000 nodes with sub-100 ms decision latency. The framework converges 3.2<InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41598_2025_14962_Article_IEq1.gif" Format="GIF" Height="13" Rendition="HTML" Resolution="72" Type="Linedraw" Width="19" /> </InlineMediaObject> <EquationSource Format="TEX">\(\times\)</EquationSource> </InlineEquation> faster than uniform sampling baselines and outperforms transformer-based models in both accuracy and inference speed. Unlike decoupled prediction-action frameworks, our method provides end-to-end optimization, enabling robust and sustainable cloud orchestration at scale.</p>

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A scalable machine learning strategy for resource allocation in database

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月20日

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  • Fady Nashat Manhary,
  • Marghny H. Mohamed,
  • Mamdouh Farouk

摘要

Modern cloud computing systems require intelligent resource allocation strategies that balance quality-of-service (QoS), operational costs, and energy sustainability. Existing deep Q-learning (DQN) methods suffer from sample inefficiency, centralization bottlenecks, and reactive decision-making during workload spikes. Transformer-based forecasting models such as Temporal Fusion Transformer (TFT) offer improved accuracy but introduce computational overhead, limiting real-time deployment. We propose LSTM-MARL-Ape-X, a novel framework integrating bidirectional Long Short-Term Memory (BiLSTM) for workload forecasting with Multi-Agent Reinforcement Learning (MARL) in a distributed Ape-X architecture. This approach enables proactive, decentralized, and scalable resource management through three innovations: high-accuracy forecasting using BiLSTM with feature-wise attention, variance-regularized credit assignment for stable multi-agent coordination, and faster convergence via adaptive prioritized replay. Experimental validation on real-world traces demonstrates 94.6% SLA compliance, 22% reduction in energy consumption, and linear scalability to over 5,000 nodes with sub-100 ms decision latency. The framework converges 3.2 \(\times\) faster than uniform sampling baselines and outperforms transformer-based models in both accuracy and inference speed. Unlike decoupled prediction-action frameworks, our method provides end-to-end optimization, enabling robust and sustainable cloud orchestration at scale.