observability instrumentation

To address insufficient observability in software systems—leading to slow fault localization and low operational efficiency—this paper designs and implements a lightweight, full-stack observability framework. The framework integrates Java bytecode instrumentation with event stream collection to enable runtime call-chain tracing, performance diagnostics, and root-cause analysis. It introduces a novel dual-mode deployment architecture supporting both online services and on-premises deployment, and achieves cross-toolchain collaborative visualization via tight REST API integration with ExplorViz. Evaluated on the TeaStore benchmark, the system delivers millisecond-scale distributed tracing and real-time heatmap rendering, reduces end-to-end latency by 32%, and shortens mean time to fault identification to the minute level. These results significantly enhance observability and operational intelligence for microservice systems.

This work addresses the optimal observability problem (OOP) in uncertain environments, which entails balancing task feasibility against sensing costs. Focusing on its decidable subproblems—sensor selection (SSP) and position observability (POP)—the paper proposes a novel solution framework based on POMDP decomposition, integrating parameter synthesis with a symbolic–subsymbolic hybrid approach. This method dramatically improves computational efficiency, scaling solvable instances by three orders of magnitude and reducing runtime by five orders of magnitude compared to prior techniques. Consequently, the approach substantially expands the tractable boundary of observability-aware planning in partially observable settings.

This study addresses the critical challenge of “silent failures”—erroneous model decisions without system crashes—in production machine learning systems, which undermine conventional monitoring and expose a gap in empirically grounded observability practices. Through seven cross-industry focus group interviews, we applied qualitative thematic coding and scenario mapping to systematically identify the types of observability data practitioners collect and their concrete uses in model validation, anomaly detection, and root-cause diagnosis. Our findings constitute the first empirical characterization of key blind spots in current ML observability tooling: delayed response to feature drift, lack of decision traceability, and difficulty quantifying business impact. Based on these insights, we propose three foundational design principles for next-generation observability tools—explanability-awareness, causal attribution support, and business-impact alignment—and establish an empirically anchored theoretical foundation for future evaluation frameworks and standardization efforts. (149 words)

In cloud-native microservices, manual and fragmented observability configuration leads to slow fault localization, high resource overhead, and degraded system performance. This paper introduces the first continuous observability assurance methodology, shifting from experience-driven to experiment-driven design. Built upon the Observability eXperimentation (OXN) framework, our approach integrates A/B testing, metric-based feedback loops, and Infrastructure-as-Code (IaC)-enabled automation to dynamically optimize and quantitatively evaluate observability configurations. Evaluated in realistic microservice deployments, our method reduces mean time to detection by 42% on average, decreases sampling overhead by 31%, and—uniquely—enables quantitative validation of how specific observability configurations directly impact Service-Level Objective (SLO) compliance. By establishing a reproducible, iterative, and empirically grounded design paradigm, this work advances observability engineering from ad hoc practice to rigorous, data-driven discipline.

This work addresses large-scale spatiotemporal systems with unknown or missing sensor models by proposing an inverse sensing architecture that synthesizes measurement likelihoods under prescribed accuracy constraints. The method minimizes information injection into the dynamic prior while ensuring the synthesized likelihood satisfies a specified error bound. Its core innovation lies in a unified maximum-entropy posterior framework for likelihood synthesis, which leverages relative entropy minimization and Radon–Nikodym derivatives to accommodate diverse discrepancy measures—including Wasserstein distance, maximum mean discrepancy (MMD), and f-divergences—and establishes a direct mapping between accuracy budgets and physical sensor configurations. Combining particle filtering with convex optimization, experiments validate the effectiveness of accuracy-constrained synthesis across four discrepancy measures, reveal how the choice of measure influences both the quantity and spatial distribution of injected information, and demonstrate successful distillation of nonparametric likelihoods into parametric forms.

Efficiently accessing internal states during large model inference is hindered by high latency and limited flexibility. This work addresses these challenges by introducing internal observability as a system-level primitive and proposing Ring², a GPU-CPU memory abstraction that decouples observation from the inference hot path through asynchronous tensor capture and a policy-driven host-export backend. The design is compatible with mainstream inference frameworks, supports flexible placement of observation points, and satisfies both service performance requirements and GPU memory constraints. Experimental results demonstrate that the approach incurs only 0.4%–6.8% overhead in offline batch processing and increases average latency by merely 6% in online serving—reducing latency overhead by 2× to 15× compared to existing solutions.

This study addresses the challenge of global linear modeling and control for highly nonlinear dynamical systems by leveraging Koopman operator theory. By introducing observable functions, the nonlinear dynamics are lifted into a higher-dimensional space where they admit an approximately linear representation. A data-driven surrogate model is constructed through a synergistic integration of Extended Dynamic Mode Decomposition (EDMD), kernelized EDMD, and machine learning techniques. The work innovatively extends the Koopman framework to input-affine systems, proposing a unified modeling approach and a corresponding Koopman-based Model Predictive Control (MPC) design methodology. Numerical simulations demonstrate that the proposed method achieves high-fidelity modeling accuracy and effective closed-loop control performance. Full reproducibility is supported by the accompanying open-source implementation.