arXiv subjects

Silviu S. Craciunas

Publications and source records attributed to Silviu S. Craciunas.

9 recordsLinked to original sources

arXiv2026

Towards Virtual Networking for a Time-Triggered Hypervisor

Virtualization enables mixed-criticality applications to run on shared hardware by isolating them within separate virtual machines (VMs). However, achieving end-to-end timing guarantees for critical tasks hosted on virtual CPUs (VCPUs) without relying on static partitioning is hard. A possible solution is a time-triggered hypervisor that dispatches VMs' VCPUs to processor cores according to a predetermined global schedule. A global schedule includes the computation, the network, and the virtualization layer to provide end-to-end timing guarantees across hosts and VMs during design time. In this work, we propose a VirtIO-based virtual network that realizes IEEE 802.1Qbv quasi-compliant inter-VM communication, providing predictable timing for critical messages exchanged between time-triggered VMs. We present our VirtIO-based virtual network architecture and derive network-level scheduling constraints that emerge from the properties of the proposed solution. We implement the proposed design in the ACRN hypervisor and evaluate the end-to-end latency of critical traffic in a loaded network, demonstrating that our time-triggered VirtIO-net prototype provides predictable end-to-end latency under load.

cs.NI↗

arXiv2026

Making Time-Sensitive Networking Deployable: A Comprehensive Lifecycle Architecture

Time-Sensitive Networking (TSN) provides deterministic guarantees and bounded latency for safety-critical applications across different domains. While the IEEE 802.1 TSN standards provide a comprehensive set of mechanisms for time synchronization, shaping, scheduling, resource reservation, and reliability, deploying TSN in real-world applications remains a complex, multi-step engineering task. Existing research primarily focuses on optimizing individual objectives, such as increasing schedulability, reducing latency, accelerating solution generation, or improving performance analysis. However, whether these extensively researched solutions can be deployed in real-world systems remains unanswered, as the assumptions made in these methodologies often differ from practical hardware constraints. Therefore, a broader, holistic view of end-to-end TSN deployment and management remains an open research question. Currently, there is a lack of a unified perspective, consensus, and coordinated research efforts focusing on a comprehensive end-to-end TSN deployment workflow. We present a comprehensive overview of the TSN deployment lifecycle, current challenges, limitations of existing tools, and future research directions for TSN deployment and management. Lastly, we identify key research gaps from a deployment perspective and provide guidance for the development of next-generation deployable TSN networks.

cs.NI↗

arXiv2026

Schedulable Job-Level Dependencies for Cause-Effect Chains via Graph Neural Networks

Modern automotive software architectures comprise large sets of mixed-criticality functions executing on shared multi-core platforms with strict real-time and end-to-end timing requirements. Sensor-to-actuator data propagation in such systems is typically expressed via cause-effect chains with worst-case data-age budgets. Job-level dependencies (JLDs) have been introduced to provide a schedule-agnostic mechanism for bounding the data age independently of the underlying scheduler. The state-of-the-art methods for synthesizing JLDs, however, do not check whether the produced JLDs are enforceable under a concrete scheduling policy or jointly schedulable at the system level. In this paper we propose the first machine-learning-based JLD synthesis method, built around a two-level Graph Neural Network with temperature-controlled sampling that learns the structural patterns connecting cause-effect chain configurations to their JLD solutions. Since learned outputs may not be correct by construction, we embed the GNN in a novel Generate-and-Verify architecture in which a safe DP data-age checker, together with a per-chain EDF feasibility checker and a system-level demand-bound test, accept or reject each candidate. We show that the ML-based generator substantially outperforms the original greedy heuristic while achieving orders-of-magnitude lower synthesis time, demonstrating that learned structural priors can effectively replace exponential propagation-tree enumeration on this class of real-time scheduling problems.

cs.SE↗

arXiv2026

TSNBench: Benchmarking LLM Proficiency in Time-Sensitive Networking

We present TSNBench, the first benchmark for evaluating large language model (LLM) proficiency in Time-Sensitive Networking (TSN), a suite of IEEE 802.1 standards for deterministic communication with bounded latency in safety-critical domains such as autonomous vehicles, aviation, defense, and industrial automation. While LLMs have been extensively evaluated on general knowledge tasks, their capabilities in safety-critical networking domains remain largely unexplored. TSNBench comprises 939 expert-validated multiple-choice questions (MCQs) covering diverse TSN mechanisms, along with 100 open-ended Worst-Case Delay (WCD) computation tasks for Credit-Based Shaper (CBS) and Cyclic Queuing and Forwarding (CQF) across varying network topologies and traffic conditions. MCQ answers are validated by domain experts, and open-ended ground truth WCD values are computed using a verified Network Calculus (NC) solver for CBS and closed-form mathematical upper bounds for CQF. We evaluate 16 LLMs and find that although models achieve 67 to 95% accuracy on MCQs, they fail substantially on open-ended WCD computation. For CBS, only GPT-5 achieves a Mean Absolute Percentage Error (MAPE) of 36.2%, meaning its predicted WCD deviates by 36.2% of the actual TSN flow delay on average, while most models exceed 80%. For CQF, the best model achieves 41.8% MAPE, with most models clustering between 80% and 100%. Such errors are large relative to TSN latency budgets and can lead to violations of real-time constraints and unsafe configurations. TSNBench demonstrates that MCQ benchmarks may overestimate LLM capabilities in safety-critical networking domains.

cs.NI↗

arXiv2022

A Real-time Calculus Approach for Integrating Sporadic Events in Time-triggered Systems

In time-triggered systems, where the schedule table is predefined and statically configured at design time, sporadic event-triggered (ET) tasks are handled within specially dedicated slots or when time-triggered (TT) tasks finish their execution early. We introduce a new paradigm for synthesizing TT schedules that guarantee the correct temporal behavior of TT tasks and the schedulability of sporadic ET tasks with arbitrary deadlines. The approach first expresses a constraint for the TT task schedule in the form of a maximal affine envelope that guarantees that as long as the schedule generation respects this envelope, all sporadic ET tasks meet their deadline. The second step consists of modeling this envelope as a burst limiting constraint and building the TT schedule via simulating a modified Least-Laxity-First (LLF) scheduler. Using this novel technique, we show that we achieve equal or better schedulability and a faster schedule generation for most use-cases compared to simple polling approaches. Moreover, we present an extension to our method that finds the most favourable schedule for TT tasks with respect to ET schedulability, thus increasing the probability of the computed TT schedule remaining feasible when ET tasks are later added or changed.

cs.NI↗

arXiv2021

Dependability-Aware Routing and Scheduling for Time-Sensitive Networking

Time-Sensitive Networking (TSN) extends IEEE 802.1 Ethernet for safety-critical and real-time applications in several areas, e.g., automotive, aerospace or industrial automation. However, many of these systems also have stringent security requirements, and security attacks may impair safety. Given a TSN-based distributed architecture, a set of applications with tasks and messages, as well as a set of security and redundancy requirements, we are interested to synthesize a system configuration such that the real-time, safety and security requirements are upheld. We use the Timed Efficient Stream Loss-Tolerant Authentication (TESLA) low-resource multicast authentication protocol to guarantee the security requirements, and redundant disjunct message routes to tolerate link failures. We consider that tasks are dispatched using a static cyclic schedule table and that the messages use the time-sensitive traffic class in TSN, which relies on schedule tables (called Gate Control Lists, GCLs) in the network switches. A configuration consists of the schedule tables for tasks as well as the disjoint routes and GCLs for messages. We propose a Constraint Programming-based formulation which can be used to find an optimal solution with respect to our cost function. Additionally, we propose a Simulated Annealing based metaheuristic, which can find good solution for large test cases. We evaluate both approaches on several test cases.

cs.NI↗

arXiv2021

Real-Time Guarantees for Critical Traffic in IEEE 802.1Qbv TSN Networks with Unscheduled and Unsynchronized End-Systems

Time-Sensitive Networking (TSN) aims to extend the IEEE 802.1Q Ethernet standard with real-time and time-aware capabilities. Each device's transmission of time-critical frames is done according to a so-called Gate Control List (GCL) schedule via the timed-gate mechanism described in IEEE 802.1Qbv. Most schedule generation mechanisms for TSN have a constraining assumption that both switches and end-systems in the network must have at least the TSN capabilities related to scheduled gates and time synchronization. However, many TSN networks use off-the-shelf end-systems, e.g., for providing sensor data, which are not scheduled and/or synchronized. In this paper, we propose a more flexible scheduling strategy that considers a worst-case delay analysis within the scheduling synthesis step, leveraging the solution's optimality to support TSN networks with unscheduled and unsynchronized end-systems while still being able to guarantee bounded latency for critical messages. Our method enables real-world systems that feature off-the-shelf microcontrollers and sensor nodes without TSN capabilities connected to state-of-the-art TSN networks to communicate critical messages in a real-time fashion. We evaluate our approach using both synthetic and real-world test cases, comparing it with existing scheduling mechanisms. Furthermore, we use OMNET++ to validate the generated GCL schedules.

cs.NI↗

arXiv2017

Formal Scheduling Constraints for Time-Sensitive Networks

In recent years, the IEEE 802.1 Time Sensitive Networking (TSN) task group has been active standardizing time-sensitive capabilities for Ethernet networks ranging from distributed clock synchronization and time-based ingress policing to frame preemption, redundancy management, and scheduled traffic enhancements. In particular the scheduled traffic enhancements defined in IEEE 802.1Qbv together with the clock synchronization protocol open up the possibility to schedule communication in distributed networks providing real-time guarantees. In this paper we formalize the necessary constraints for creating window-based IEEE~802.1Qbv Gate Control List schedules for Time-sensitive Networks (TSN). The resulting schedules allow a greater flexibility in terms of timing properties while still guaranteeing deterministic communication with bounded jitter and end-to-end latency.

cs.NI↗

arXiv2014

Concurrency and Scalability versus Fragmentation and Compaction with Compact-fit

We study, formally and experimentally, the trade-off in temporal and spatial overhead when managing contiguous blocks of memory using the explicit, dynamic and real-time heap management system Compact-fit (CF). The key property of CF is that temporal and spatial overhead can be bounded, related, and predicted in constant time through the notion of partial and incremental compaction. Partial compaction determines the maximally tolerated degree of memory fragmentation. Incremental compaction of objects, introduced here, determines the maximal amount of memory involved in any, logically atomic, portion of a compaction operation. We explore CF's potential application space on (1) multiprocessor and multicore systems as well as on (2) memory-constrained uniprocessor systems. For (1), we argue that little or no compaction is likely to avoid the worst case in temporal as well as spatial overhead but also observe that scalability only improves by a constant factor. Scalability can be further improved significantly by reducing overall data sharing through separate instances of Compact-fit. For (2), we observe that incremental compaction can effectively trade-off throughput and memory fragmentation for lower latency.

cs.PL↗