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<p>The rise of 5G networks and the Internet of Things (IoT) is driving a shift towards edge computing, where applications increasingly consist of complex, containerized workflows. Existing orchestration platforms, designed for centralized data centers, are reactive, workflow-agnostic, and poorly scalable, leading to high makespan, energy consumption, and cost. This paper presents Cerebrum, a proactive, learning-based framework for multi-objective scheduling of containerized workflows at the edge. Cerebrum combines several key innovations: (1) hierarchical workflow decomposition for scalable DAG partitioning, (2) a proactive multi-objective reinforcement learning agent with predictive resource forecasting, (3) a two-tiered architecture separating global inter-cluster orchestration from local intra-cluster scheduling, and (4) a unified policy framework balancing performance, energy, and cost. Extensive simulations show that Cerebrum achieves the lowest average makespan (90&#xa0;s, 5.3% improvement over FL-Proactive), reduces energy consumption to 1,950&#xa0;kJ (6.0% lower), and cuts monetary cost to $2.4 (36.8% lower), while maintaining scheduling decision latency below 15&#xa0;ms across 20 clusters. These results demonstrate that proactive, adaptive scheduling intelligence can simultaneously optimize multiple objectives, providing a robust and scalable solution for next-generation edge-cloud applications.</p>

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Cerebrum: A Proactive Federated Learning Framework for Multi-Objective Edge Workflow Scheduling

  • 期刊论文

  • 发表日期：

    2025年11月19日

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  • Seyed Hossein Ahmadpanah,
  • Meghdad Mirabi

摘要

The rise of 5G networks and the Internet of Things (IoT) is driving a shift towards edge computing, where applications increasingly consist of complex, containerized workflows. Existing orchestration platforms, designed for centralized data centers, are reactive, workflow-agnostic, and poorly scalable, leading to high makespan, energy consumption, and cost. This paper presents Cerebrum, a proactive, learning-based framework for multi-objective scheduling of containerized workflows at the edge. Cerebrum combines several key innovations: (1) hierarchical workflow decomposition for scalable DAG partitioning, (2) a proactive multi-objective reinforcement learning agent with predictive resource forecasting, (3) a two-tiered architecture separating global inter-cluster orchestration from local intra-cluster scheduling, and (4) a unified policy framework balancing performance, energy, and cost. Extensive simulations show that Cerebrum achieves the lowest average makespan (90 s, 5.3% improvement over FL-Proactive), reduces energy consumption to 1,950 kJ (6.0% lower), and cuts monetary cost to $2.4 (36.8% lower), while maintaining scheduling decision latency below 15 ms across 20 clusters. These results demonstrate that proactive, adaptive scheduling intelligence can simultaneously optimize multiple objectives, providing a robust and scalable solution for next-generation edge-cloud applications.