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arXiv · 2602.17282

Visual Insights into Agentic Optimization of Pervasive Stream Processing Services

Boris Sedlak·Víctor Casamayor Pujol·Schahram Dustdar
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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.

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Boris Sedlak, Víctor Casamayor Pujol, Schahram Dustdar. 2026-02-19. Visual Insights into Agentic Optimization of Pervasive Stream Processing Services. https://arxiv.org/abs/2602.17282

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