[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-81782-en":3,"doc-seo-81782-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},81782,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Stochastic Connectivity as the Foundation of a Runtime Model for Microservice Availability Analysis","Microservice availability is often evaluated via fault injection and chaos experiments, which are expensive, disruptive, and hard to repeat after each architectural change. Distributed tracing and deployment metadata offer cheaper evidence but typically remain descriptive rather than endpoint-level predictive. This work introduces a formal runtime availability model grounded in stochastic connectivity, separating replica computational failures from dependency communication failures while enabling Monte Carlo analysis and trace-based model reconstruction.","Stochastic Connectivity as the Foundation of a Runtime Model for Microservice Availability Analysis  \nAnatoly A. Krasnovsky  \nInnopolis University Innopolis, Russia MB3R Lab Innopolis, Russia  \nAnna Maslovskaya  \nInnopolis University Innopolis, Russia  \narXiv :2607 .00740v 1 [ cs . SE] 1 Jul 2026  \nAbstract  \nMicroservice 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.  \nKeywords  \nruntime models, stochastic models, edge reliability, availability, resilience analysis, distributed tracing, model discovery, microservices, chaos engineering  \n1 Introduction  \nMicroservice systems are routinely described as graphs of services, queues, databases, gateways, and APIs. This graph intuition is operationally useful, but it is too informal for availability reasoning. A dependency map obtained from tracing or a service mesh can show that service 􀀰 called service 􀀱, yet it does not define whether the dependency was blocking, whether at least one replica of 􀀱 is sufficient, whether the call matters for a particular endpoint, or which network and routing failures are included in the probability space. As a result, teams often fall back to fault injection and chaos engineering to learn availability behavior empirically [3, 15] . Such experiments are valuable, but they are costly, disruptive, and hard to repeat for every dependency or replication change.  \nModel-driven engineering offers a different path: make the abstraction explicit, give it semantics, and analyze the model before  \nexecuting failures in the system. This is especially natural for runtime models, which abstract monitored system state and operational context for analysis and adaptation [6, 13] . Microservice platforms already generate the raw material for such models through distributed traces, deployment manifests, service-mesh telemetry, andobservability metadata. The missing step is a compact formal model that explains which availability question the reconstructed graph answers. From a resilience perspective, endpoint availability under explicit fault scenarios is the measurable quantity: given a fault model over replicas and communication dependencies, the model asks whether an endpoint still satisfies its success predicate.  \nThe route taken here is synthetic: we adapt probabilistic connectivity reasoning from graph reliability to the runtime-model setting of trace-observed microservice systems, and combine it with deployment-level replication and endpoint-specific success predicates. This paper proposes a runtime availability model based on stochastic connecti","cbCaidtYUnIZUgTN","https://ap.wps.com/l/cbCaidtYUnIZUgTN","pdf",623555,5,1,7,"English","en",105,"# Introduction\n## Motivation and problem with existing approaches\n## Model-driven engineering and runtime models\n# Runtime availability model based on stochastic connectivity\n## System representation and semantics","[{\"question\":\"Why do fault injection and chaos experiments fall short for microservice availability analysis?\",\"answer\":\"They are costly, operationally risky, and difficult to repeat for every architectural change or dependency/replication adjustment.\"},{\"question\":\"How does the proposed model turn tracing and deployment metadata into endpoint-level availability insights?\",\"answer\":\"It reconstructs a typed service-dependency graph, replication map, probability measure over node/edge states, and request-specific success predicates, then estimates the probability that an endpoint predicate holds under sampled failures.\"},{\"question\":\"What key distinction does the model make regarding failures and dependencies?\",\"answer\":\"It separates computational failures of service replicas from communication failures of logical dependencies, showing that replication cannot compensate for bottleneck 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