Application level observability for adaptive Edge to Cloud continuum systems

2025-12-01

International Conference on Utility and Cloud Computing

AI Summary

This work addresses the lack of fine-grained observability in edge-to-cloud continuum systems operating under heterogeneous and dynamic conditions, which hinders adaptive control needed to meet performance objectives. The paper proposes an application-level observability framework that uniquely integrates developer-instrumented telemetry with SLO-aware feedback mechanisms to enable real-time monitoring and autonomous regulation across the edge–cloud continuum. Built upon OpenTelemetry, Prometheus, K3s, and Chaos Mesh, the framework delivers end-to-end observability and elasticity management. Evaluated in a video processing scenario, it significantly enhances system scalability, fault tolerance, and responsiveness, effectively preserving critical quality-of-service metrics such as frame rate, latency, and detection accuracy.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Planning, Routing, and Scheduling: Plan Execution and Monitoring
• Computer Vision: Interpretability, Explainability, and Transparency

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Cloud, edge and content delivery systems for the Web
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Modern Edge-to-Cloud (E2C) systems require fine-grained observability to ensure adaptive behavior and compliance with performance objectives across heterogeneous and dynamic environments. This work introduces an application-level observability framework that integrates developer-driven instrumentation and SLO-aware feedback for autonomous adaptation. By combining OpenTelemetry, Prometheus, K3s, and Chaos Mesh, the framework enables real-time monitoring and adaptive control across the continuum. A video processing use case demonstrates how application-level metrics guide automatic adjustments to maintain target frame rate, latency, and detection accuracy under variable workloads and injected faults. Preliminary results highlight improved scalability, fault tolerance, and responsiveness, providing a practical foundation for adaptive, SLO-compliant E2C applications.

Problem

• Edge-to-Cloud
• observability
• adaptive systems
• SLO compliance
• application-level monitoring

Innovation

• application-level observability
• SLO-aware adaptation
• Edge-to-Cloud continuum
• autonomous adaptation
• developer-driven instrumentation