[Paper] Visual Insights into Agentic Optimization of Pervasive Stream Processing Services

Source: arXiv - 2602.17282v1

Overview

The paper introduces a demo platform that lets developers auto‑scale stream‑processing services running on edge devices (think smart‑city sensors, IoT gateways, etc.). By exposing service‑specific control knobs and feeding them to a learning‑based scaling agent, the system can continuously adapt to fluctuating workloads and limited resources—crucial for low‑latency, pervasive applications.

Key Contributions

• Context‑aware autoscaling framework for heterogeneous stream‑processing services on edge nodes.
• Unified monitoring & control APIs that expose per‑service parameters (e.g., thread count, batch size, sampling rate).
• Exploratory learning agent that incrementally discovers each service’s action space and builds a performance model on‑the‑fly.
• Open‑source artifact repository with demo videos, a poster, and extensible code so developers can plug in custom agents.
• Live demo showcasing real‑time scaling decisions and visual feedback of resource allocation across co‑located services.

Methodology

1. Platform Setup – Multiple stream‑processing services (e.g., filtering, aggregation, anomaly detection) are containerized and deployed on a shared edge device. Each service publishes a set of tunable parameters via a lightweight REST/WS interface.
2. Monitoring Layer – A central monitor collects metrics such as CPU/memory usage, input rate, processing latency, and QoS targets.
3. Scaling Agent – The agent starts with no prior knowledge. It explores the parameter space by applying small perturbations, observes the resulting performance impact, and updates an internal model (e.g., Bayesian optimization or reinforcement‑learning policy).
4. Optimization Loop – Using the learned model, the agent selects parameter configurations that satisfy latency constraints while minimizing resource consumption, and it continuously re‑evaluates as workload or resource availability changes.
5. Visualization – A dashboard visualizes per‑service metrics, current parameter settings, and the agent’s confidence in its model, making the learning process transparent to developers.

Results & Findings

• The agent reduced end‑to‑end latency by up to 35 % compared to static provisioning, while keeping CPU usage within 70 % of the device’s capacity.
• Services that previously contended for resources (e.g., two heavy aggregators) learned to stagger their scaling actions, eliminating “resource cannibalism.”
• The exploration phase converged quickly: after ~15 configuration trials per service, the agent achieved near‑optimal settings, demonstrating feasibility for dynamic edge environments.
• Developers could override or guide the agent via the exposed APIs, enabling hybrid human‑in‑the‑loop control.

Practical Implications

• Edge‑native autoscaling: Operators of smart‑city infrastructures, industrial IoT, or AR/VR edge pipelines can deploy this framework to maintain strict latency SLAs without over‑provisioning hardware.
• Developer productivity: The unified control interface abstracts away the intricacies of each service’s tuning knobs, letting engineers focus on business logic rather than manual performance tweaking.
• Cost savings: By automatically throttling resources when demand drops, organizations can extend the lifespan of edge hardware and reduce energy consumption.
• Extensibility: The open artifact repository encourages the community to plug in alternative learning algorithms (e.g., multi‑armed bandits, deep RL) or integrate with orchestration platforms like Kubernetes‑Edge or OpenYurt.
• Safety & compliance: The visual dashboard provides audit trails of scaling decisions, which is valuable for regulated domains (e.g., healthcare IoT) where deterministic behavior is required.

Limitations & Future Work

• The current demo runs on a single edge node; scaling the approach to multi‑node edge clusters and handling network‑level resource contention remains an open challenge.
• The exploration strategy assumes relatively smooth performance surfaces; services with highly non‑linear or discontinuous behavior may need more sophisticated probing or prior domain knowledge.
• Security considerations (e.g., protecting the control APIs from malicious tampering) are not addressed in depth.
• Future work includes integrating with container orchestration schedulers, extending the agent to support multi‑objective optimization (e.g., latency vs. energy), and evaluating the system on larger, real‑world smart‑city deployments.

Authors

• Boris Sedlak
• Víctor Casamayor Pujol
• Schahram Dustdar

Paper Information

• arXiv ID: 2602.17282v1
• Categories: cs.DC, cs.PF, eess.SY
• Published: February 19, 2026
• PDF: Download PDF