This survey examines the sources, propagation mechanisms, and observable impacts of timing variability in microservice-based systems, using software-defined vehicles as a motivating example, and compares major classes of timing models by their assumptions about workload stability, execution structure, resource sharing, observability, and guarantee admissibility.
Abstract
Microservice-based systems are modular and adaptable, but their distributed structure makes their timing behavior difficult to analyze and guarantee. Because latency emerges from interactions among service dependencies, shared resources, and coordinating middleware, local timing disturbances can propagate into system-level effects. This survey examines the sources, propagation mechanisms, and observable impacts of timing variability in microservice-based systems, using software-defined vehicles as a motivating example. It compares major classes of timing models by their assumptions about workload stability, execution structure, resource sharing, observability, and guarantee admissibility, and summarizes their limitations within microservice-based software-defined vehicles.
A formal verification framework that consists of a combination of specifications of temporal logic, model checking and modeling of service interactions graphs to study the behavior of the system and confirm the correctness properties is suggested.
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This paper presents an experimental framework combining probabilistic design-time timing analysis with a monitored Kuksa-based implementation, and introduces a comparison methodology separating conservative coverage from predictive fidelity of timing distributions.
Modern cloud-native microservice systems face significant challenges when deployed in ultra-large-scale, missioncritical environments such as civil aviation passenger service platforms. Existing SDK-based frameworks suffer from tight coupling and limited cross-language flexibility, while Service Mesh solutions introduc...
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This paper addresses this by proposing an extension of the Arrowhead framework to bridge synchronization between instances to the service level without losing any abstraction, thus preserving fundamental achievements of the Arrowhead, such as late binding and loose coupling.
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SOAFEE encounters real-time challenges in resource-constrained automotive systems, where improper container configuration can cause catastrophic timing failures. This paper presents an automated method for guaranteeing endto-end latency in containerized autonomous driving systems. We model the system as a hierarchical...
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FAA+RTS is a novel solution as it integrates previously-isolated methods for dealing with multiple constraints into a single framework, presenting a single overview of all possible trade-offs to the application designer, resulting in a robust and reliable system.
L. Miedema, D. Sapra, P. Novobilsky et al.· 0 citations
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