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N. Meratnia

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Review Aug 2026

A Survey of Timing Variability in Microservice-Based Software-Defined Vehicles

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.

Cyrus K. Vattes, H. Mostafaei, N. Meratnia · 0 citations
Preprint Jul 2026

AutoEncoder-Compressed Parallel Split Learning for Pre-trained Model Fine-Tuning

AE-PSL is proposed, a communication-efficient PSL framework that compresses intermediate activations and gradients using a lightweight AutoEncoder placed at the split layer and introduces a novel two-stage alignment mechanism, which adapts the AE to the pre-trained model's feature manifold and client-specific feature distributions before DFT.

Bas Meuwissen, Vasileios Tsouvalas, N. Meratnia · 1 citation