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.

翻译：边缘设备资源有限，这不可避免地导致流处理服务无法满足其需求的情况。现有弹性伸缩机制完全聚焦于资源伸缩，而边缘设备需要其他方式来维持竞争性服务的服务等级目标（SLO）。为解决这些问题，我们提出了一种多维弹性伸缩平台（MUDAP），支持在服务级和资源级维度进行细粒度的垂直伸缩。MUDAP支持针对可用参数定制服务特定的伸缩策略，例如调整特定服务的数据质量或模型规模。为优化跨服务的执行过程，我们提出了一种基于结构知识回归分析（RASK）的伸缩代理。RASK代理高效地探索解空间，并学习处理环境的连续回归模型，以推断最优伸缩动作。我们将所提方法与Kubernetes VPA和强化学习代理两种自动伸缩器进行比较，在单个边缘设备上对最多9个服务进行伸缩测试。结果表明，RASK仅需20次迭代（即观察200秒处理时间）即可推断出准确的回归模型。通过逐步增加弹性维度，RASK在最高请求负载下相比基准方法减少了28%的SLO违反率。