基于多专家共识机制的无服务器环境自动缩放方法
An Auto-Scaling Approach for Serverless Environments Based on a Multi-Expert Consensus Mechanism
浏览论文内容
中文总结 AI 辅助
针对无服务器环境自动缩放难题，提出依赖感知自动缩放框架，集成多种技术，通过识别重要函数、预测资源需求、多模型共识及成本感知控制进行缩放决策，实验证明该方法能有效降低预测误差并减少成本。
中文摘要 AI 辅助
无服务器计算提供自动资源管理和按使用付费执行，但由于动态工作负载、冷启动延迟和函数间依赖关系，有效的自动缩放仍具挑战性。我们提出一种依赖感知自动缩放框架，集成基于图的瓶颈识别、短期工作负载预测、多模型共识和成本感知缩放控制。将无服务器应用表示为有向依赖图，用加权度中心性识别重要函数，用轻量级模型预测资源需求，通过性能加权概率集成组合输出。控制器结合冷启动感知和成本比较进行缩放决策。实验表明监督预测在自动缩放决策生成上优于无监督聚类，所提集成方法预测准确率达99.88%，与代表性混合预测方法相比降低了预测误差。跨多个云定价模型的评估表明在保持性能目标的同时能持续降低基础设施成本。结果表明结合依赖分析、多专家预测和成本感知控制为无服务器自动缩放提供了一个强大且实用的解决方案。
英文摘要
Serverless computing provides automatic resource management and pay-per-use execution, but effective autoscaling remains challenging because of dynamic workloads, cold-start latency, and dependencies among functions. We present a dependency-aware autoscaling framework that integrates graph-based bottleneck identification, short-term workload forecasting, multi-model consensus, and cost-aware scaling control. Serverless applications are represented as directed dependency graphs, and structurally important functions are identified using weighted degree centrality. Resource demand is predicted using lightweight MLP, LSTM, and CNN models. Their outputs are combined through a performance-weighted probabilistic ensemble inspired by Bayesian model averaging. The controller further incorporates cold-start awareness and cost comparison to select among scale-up, scale-down, and hold actions. Experiments using real workload traces show that supervised forecasting substantially outperforms unsupervised clustering for autoscaling decision generation. The proposed ensemble achieves 99.88 percent prediction accuracy and reduces prediction error compared with representative hybrid forecasting methods. Evaluations across multiple cloud pricing models also demonstrate consistent infrastructure cost reductions while maintaining performance targets. The results show that combining dependency analysis, multi-expert forecasting, and cost-aware control provides a robust and practical solution for serverless autoscaling.
