This paper proposes a hierarchical solution to scale streaming services across quality and resource dimensions. Modern scenarios, like smart cities, heavily rely on the continuous processing of IoT data to provide real-time services and meet application targets (Service Level Objectives -- SLOs). While the tendency is to process data at nearby Edge devices, this creates a bottleneck because resources can only be provisioned up to a limited capacity. To improve elasticity in Edge environments, we propose to scale services in multiple dimensions -- either resources or, alternatively, the service quality. We rely on a two-layer architecture where (1) local, service-specific agents ensure SLO fulfillment through multi-dimensional elasticity strategies; if no more resources can be allocated, (2) a higher-level agent optimizes global SLO fulfillment by swapping resources. The experimental results show promising outcomes, outperforming regular vertical autoscalers, when operating under tight resource constraints.

翻译：本文提出了一种分层解决方案，用于在服务质量与资源维度上扩展流处理服务。现代应用场景（如智慧城市）高度依赖物联网数据的持续处理以提供实时服务并满足应用目标（服务级别目标——SLOs）。尽管当前趋势倾向于在邻近的边缘设备上处理数据，但这会形成瓶颈，因为资源仅能按有限容量进行配置。为提升边缘环境中的弹性伸缩能力，我们提出在多维度上扩展服务——既可通过资源维度，也可通过服务质量维度。我们采用双层架构：（1）本地化、服务专用的代理通过多维弹性伸缩策略确保SLO达成；若无法分配更多资源，（2）高层级代理通过资源置换优化全局SLO达成。实验结果表明，在严格资源约束条件下运行时，该方案展现出优于常规纵向自动伸缩器的性能表现。