面向普及流处理服务的代理优化可视化
Visual Insights into Agentic Optimization of Pervasive Stream Processing Services
AI总结：
本研究提出了一种面向普及流处理服务的自动扩展平台，通过代理优化服务执行，提升资源利用率和系统性能。
摘要：
在数据源附近处理传感数据，通常涉及边缘设备，为普及应用（如智慧城市）提供了低延迟的潜力。这通常涉及大量的处理服务，这些服务在资源有限的情况下执行；这种设置面临三个问题：首先，应用程序需求和资源可用性波动，因此服务执行必须动态扩展以维持处理需求（例如，延迟）；其次，每个服务允许不同的操作来调整其运行，因此它们需要单独的扩展策略；第三，如果没有更高层次的协调者，服务会消耗同一设备上其他服务的资源。该演示首先展示了一个面向情境的自动扩展平台，允许开发人员在多个服务特定参数上监控和调整服务执行。然后，我们将一个扩展代理连接到这些接口，通过探索每个服务的操作空间逐步了解处理环境；代理随后根据这种知识优化服务执行。参与者可以回看演示内容作为视频摘要和介绍海报，或通过扩展制品库构建自定义代理。
英文摘要：
Processing sensory data close to the data source, often involving Edge devices, promises low latency for pervasive applications, like smart cities. This commonly involves a multitude of processing services, executed with limited resources; this setup faces three problems: first, the application demand and the resource availability fluctuate, so the service execution must scale dynamically to sustain processing requirements (e.g., latency); second, each service permits different actions to adjust its operation, so they require individual scaling policies; third, without a higher-level mediator, services would cannibalize any resources of services co-located on the same device. This demo first presents a platform for context-aware autoscaling of stream processing services that allows developers to monitor and adjust the service execution across multiple service-specific parameters. We then connect a scaling agent to these interfaces that gradually builds an understanding of the processing environment by exploring each service's action space; the agent then optimizes the service execution according to this knowledge. Participants can revisit the demo contents as video summary and introductory poster, or build a custom agent by extending the artifact repository.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
