边缘设备上多维弹性扩展，提升流服务处理能力与稳定性
Multi-Dimensional Autoscaling of Stream Processing Services on Edge Devices
- 支持服务与资源双维度细粒度垂直扩展，按需调整数据质量或模型大小
- 仅20次迭代（200秒）即构建精准回归模型，比基线减少28%服务目标违例
- 适合资源受限的边缘计算场景，尤其适用于多服务并发部署
边缘设备资源有限，导致流处理服务难以满足需求。现有自动扩缩机制仅关注资源层面扩展，而边缘环境需兼顾多个竞争服务的性能目标。为此，我们提出多维自动扩展平台MUDAP，支持服务级与资源级的细粒度垂直扩展，可针对不同服务灵活调节数据质量或模型规模。为优化跨服务执行，引入基于结构知识回归分析（RASK）的扩缩代理，高效探索解空间并学习连续回归模型以推断最优扩缩动作。在单个边缘设备上对最多9个服务进行对比实验，结果表明：RASK仅需20次迭代（即观测200秒处理过程）即可建立高精度回归模型；随着弹性维度增加，其相比基线（Kubernetes VPA与强化学习代理）在维持更高请求负载时，服务目标违例减少28%。
原文摘要 · Abstract (English)
Edge devices have limited resources, which inevitably leads to situations where stream processing services cannot satisfy their needs. While existing autoscaling mechanisms focus entirely on resource scaling, Edge devices require alternative ways to sustain the Service Level Objectives (SLOs) of competing services. To address these issues, we introduce a Multi-dimensional Autoscaling Platform (MUDAP) that supports fine-grained vertical scaling across both service- and resource-level dimensions. MUDAP supports service-specific scaling tailored to available parameters, e.g., scale data quality or model size for a particular service. To optimize the execution across services, we present a scaling agent based on Regression Analysis of Structural Knowledge (RASK). The RASK agent efficiently explores the solution space and learns a continuous regression model of the processing environment for inferring optimal scaling actions. We compared our approach with two autoscalers, the Kubernetes VPA and a reinforcement learning agent, for scaling up to 9 services on a single Edge device. Our results showed that RASK can infer an accurate regression model in merely 20 iterations (i.e., observe 200s of processing). By increasingly adding elasticity dimensions, RASK sustained the highest request load with 28% less SLO violations, compared to baselines.
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