Visual Insights into Agentic Optimization of Pervasive Stream Processing Services

TL;DR

This paper introduces a platform and agent for context-aware, autonomous scaling of edge stream processing services, addressing resource fluctuation and multi-service coordination in pervasive computing environments.

Contribution

It presents a novel platform for monitoring and adjusting service parameters and a scaling agent that learns and optimizes resource allocation autonomously.

Findings

Effective dynamic scaling of services demonstrated

Agent successfully learns environment and optimizes resource use

Platform supports custom agent development

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.