Stochastic Connectivity as the Foundation of a Runtime Model for Microservice Availability Analysis

• URL: http://arxiv.org/abs/2607.00740v1
• Date: Wed, 01 Jul 2026 10:23:00 GMT
• Title: Stochastic Connectivity as the Foundation of a Runtime Model for Microservice Availability Analysis
• Abstract summary: This paper proposes a formal runtime availability model based on connectivity for resilience-oriented analysis of microservice endpoints.<n>It treats endpoint availability under explicit fault scenarios as a measurable facet of microservice resilience, combining a typed service-dependency graph, a replication map, a probability measure over node and edge states, and request-specific success predicates.<n>The model can be reconstructed from traces and deployment artifacts, parameterized for architectural what-if analysis, and analyzed by Monte Carlo simulation before or alongside fault injection.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Microservice availability is commonly assessed by fault injection and chaos experiments, but such experiments are costly, operationally risky, and difficult to repeat for every architectural change. Distributed tracing and deployment metadata provide cheaper evidence, yet they usually remain descriptive: they show which services interacted, not what endpoint-level availability property follows. This paper proposes a formal runtime availability model based on stochastic connectivity for resilience-oriented analysis of microservice endpoints. It treats endpoint availability under explicit fault scenarios as a measurable facet of microservice resilience, combining a typed service-dependency graph, a replication map, a probability measure over node and edge states, and request-specific success predicates. Its semantics separates computational failures of service replicas from communication failures of logical dependencies, showing that replication cannot compensate for bottleneck dependencies. The model can be reconstructed from traces and deployment artifacts, parameterized for architectural what-if analysis, and analyzed by Monte Carlo simulation before or alongside fault injection. We define the model, its trace-to-model construction, elementary semantic properties, and a synthetic adequacy study. The study matches closed-form oracle cases within sampling error and exposes boundaries caused by edge bottlenecks, correlated failures, missing traces, and time-dependent failures.

Related papers

• Adaptive Recurrent Message Passing for Test Time Computing on Graphs [80.56829418752513]
  We show that recurrent graph models can overcome the intrinsic mismatch between graph data and architectural designs.<n>We propose AdaR, an Adaptive Recurrent graph model, empowering flexible test-time computing on various downstream tasks without changing model parameters.
  arXiv  Detail & Related papers  (2026-06-21T12:14:11Z)
• UModel: An Agent-Ready Observability Data Modeling Method at Scale [25.076726153200084]
  We present UModel, a unified ontological framework that shifts observability from data-centric to object-centric modeling.<n>By re-modeling the "AIOps 2025 Challenge" dataset using UModel, the precision of root cause localization improved by 8%.<n>UModel has served tens of thousands of users, sustained millions of operations per second, and delivered sub-second query latency.
  arXiv  Detail & Related papers  (2026-06-03T12:25:50Z)
• Joint Model and Data Sparsification via the Marginal Likelihood [53.29070892356214]
  We propose jointly learning individual feature and sample relevancies, enabling simultaneous model and data sparsification.<n>This symmetric pruning of model and data offers a natural extension that preserves conjugacy.<n> Empirical results across diverse regression tasks affirm that a joint ARD approach consistently yields both sparse and robust prediction models.
  arXiv  Detail & Related papers  (2026-05-28T13:26:53Z)
• CellScientist: Dual-Space Hierarchical Orchestration for Closed-Loop Refinement of Virtual Cell Models [62.281480231694]
  Virtual Cell Modeling (VCM) requires models that not only predict perturbation responses, but also support targeted revision when predictions fail.<n>Current LLM-assisted modeling face a refinement-routing problem: prediction discrepancies are observed through executable implementations.<n>We propose CellScientist, a dual-space hierarchical framework that couples a high-level hypothesis space with a low-level executable implementation space.
  arXiv  Detail & Related papers  (2026-05-08T06:40:24Z)
• Diagnosing Structural Failures in LLM-Based Evidence Extraction for Meta-Analysis [0.8193467416247519]
  Review and meta-analyses rely on converting narrative articles into structured, numerically grounded study records.<n>Despite rapid advances in large language models (LLMs), it remains unclear whether they can meet the structural requirements of this process.<n>We propose a structural, diagnostic framework that evaluates LLM-based evidence extraction as a progression of schema-constrained queries.
  arXiv  Detail & Related papers  (2026-02-11T14:09:43Z)
• Evaluating Asynchronous Semantics in Trace-Discovered Resilience Models: A Case Study on the OpenTelemetry Demo [0.0]
  We revisit a trace-discovered connectivity model that derives a service dependency graph from traces.<n>We use Monte Carlo simulation to estimate endpoint availability under fail-stop service failures.
  arXiv  Detail & Related papers  (2025-12-13T13:08:05Z)
• Graph Neural AI with Temporal Dynamics for Comprehensive Anomaly Detection in Microservices [7.957284443727372]
  This study addresses the problem of anomaly detection and root cause tracing in microservice architectures.<n>It proposes a unified framework that combines graph neural networks with temporal modeling.
  arXiv  Detail & Related papers  (2025-11-05T08:28:41Z)
• Model Discovery and Graph Simulation: A Lightweight Gateway to Chaos Engineering [0.0]
  Chaos engineering reveals resilience risks but is expensive and operationally risky to run broadly and often.<n>We claim that a simple connectivity-only topological model can provide fast, low-risk availability estimates under fail-stop faults.
  arXiv  Detail & Related papers  (2025-06-12T10:59:28Z)
• Multi-modal Causal Structure Learning and Root Cause Analysis [67.67578590390907]
  We propose Mulan, a unified multi-modal causal structure learning method for root cause localization. We leverage a log-tailored language model to facilitate log representation learning, converting log sequences into time-series data. We also introduce a novel key performance indicator-aware attention mechanism for assessing modality reliability and co-learning a final causal graph.
  arXiv  Detail & Related papers  (2024-02-04T05:50:38Z)
• Stateful Offline Contextual Policy Evaluation and Learning [88.9134799076718]
  We study off-policy evaluation and learning from sequential data. We formalize the relevant causal structure of problems such as dynamic personalized pricing. We show improved out-of-sample policy performance in this class of relevant problems.
  arXiv  Detail & Related papers  (2021-10-19T16:15:56Z)
• Structural Causal Models Are (Solvable by) Credal Networks [70.45873402967297]
  Causal inferences can be obtained by standard algorithms for the updating of credal nets. This contribution should be regarded as a systematic approach to represent structural causal models by credal networks. Experiments show that approximate algorithms for credal networks can immediately be used to do causal inference in real-size problems.
  arXiv  Detail & Related papers  (2020-08-02T11:19:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.