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.

翻译：现代边缘到云（E2C）系统需要细粒度的可观测性，以确保在异构和动态环境中实现自适应行为并满足性能目标。本文提出了一种应用级可观测性框架，该框架集成了开发者驱动的插装和面向服务等级目标（SLO）的反馈机制，以实现自主适应。通过结合 OpenTelemetry、Prometheus、K3s 和 Chaos Mesh，该框架能够在整个连续体上实现实时监控和自适应控制。一个视频处理用例展示了应用级指标如何引导系统进行自动调整，以在可变工作负载和注入故障的情况下维持目标帧率、延迟和检测精度。初步结果突显了该系统在可扩展性、容错能力和响应性方面的提升，为构建自适应的、符合SLO的E2C应用提供了实用基础。