Visual Insights into Agentic Optimization of Pervasive Stream Processing Services

2026-02-19

AI Summary

This work addresses the challenges of resource contention and performance instability in edge stream processing under dynamic workloads and fluctuating resources, which often stem from the absence of a unified coordination mechanism. To this end, we propose the first framework that integrates embodied intelligent agents into collaborative optimization for edge stream processing. Our approach features a context-aware autoscaling platform that unifies service-specific policies with global resource scheduling through an extensible monitoring interface and a multi-service action space exploration mechanism. Leveraging reinforcement learning–driven agents, the framework significantly enhances resource efficiency and response timeliness on real-world edge platforms, while also supporting user-defined policies and enabling reproducible experiments with visual analytics.

Technology Category

• Intelligent Robots: Embodied AI
• Multiagent Systems: Coordination and Collaboration
• Machine Learning: Learning on the Edge & Model Compression

Application Category

• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• stream processing
• autoscaling
• edge computing
• resource allocation
• service optimization

Innovation

• agentic optimization
• context-aware autoscaling
• edge stream processing
• service-specific scaling
• resource coordination