Cyclic Queuing and Forwarding (CQF) is an increasingly adopted mechanism in Time-Sensitive Networking (TSN) for bounding end-to-end delays through fixed-length cycles with alternating transmission queues. While timing guarantees for CQF flows are well established under both time-triggered (TT) and event-triggered (ET) implementations, the worst-case interference that CQF may impose on other traffic classes in mixed-criticality TSN networks remains an open problem. This challenge is exacerbated by the structured, non-work-conserving behavior of CQF and the complexity of its interaction with heterogeneous TSN scheduling mechanisms such as TAS, CBS, and SP. This paper presents the first formal framework for quantifying the worst-case interference caused by CQF on other schedulers under both TT- and ET-based implementations. We propose a network-calculus-based CQF real-time interface abstraction that models the residual service available to coexisting traffic. We formally derive closed-form upper bounds on CQF-induced interference, explicitly capturing bidirectional interactions between CQF and both higher- and lower-priority traffic classes. These bounds can be modularly and seamlessly integrated into existing schedulability analyses, enabling scalable and compositional timing verification in hybrid TSN architectures. Extensive evaluations on synthetic benchmarks and realistic TSN configurations demonstrate the analytical effectiveness, scalability, and practical applicability of the proposed framework in certifying end-to-end guarantees in mixed-criticality TSN systems.
Time-Sensitive Networking enables deterministic communication in cyber-physical systems using time-aware shapers governed by Gate Control Lists (GCL). Although this mechanism improves reliability, it also introduces vulnerabilities exploitable through schedule-based attacks. We show that, by analyzing traffic patterns, adversaries can estimate GCL parameters and reconstruct the schedule to inject precisely timed traffic. These injections can cause priority inversions, where low-priority flows delay high-priority traffic, degrading latency and schedulability. Such schedule-based attacks are particularly stealthy as conventional Intrusion Detection Systems (IDS) may fail to detect them. We implement this attack and conduct randomized experiments to evaluate the impact on synthetic workloads and on off-the-shelf hardware.
Deploying large language models (LLMs) on edge devices enables real-time responses while preserving user privacy. However, constrained memory and compute resources pose significant challenges for high-quality, single-device inference. To address this, we propose HCInfer, a hierarchical coordination framework for collaborative LLM inference across edge devices. By leveraging idle neighboring devices, HCInfer alleviates performance bottlenecks typical in isolated deployments. HCInfer employs a two-level coordination strategy. At the inter-device level, it leverages idle neighboring devices to collaboratively process attention computations, significantly reducing synchronization overhead. At the intra-device level, it applies finegrained memory and compute optimizations to fully exploit local hardware capabilities. Building on this architecture, HCInfer integrates three key components: (1) Asymmetric Transformer decomposition decouples attention and FFN computation, enabling selective and parallel execution across devices. (2) Layer-wise subdeadline scheduling dynamically profiles execution latency and adapts precision or structure to meet real-time constraints (3) An Overhead Mitigation Module efficiently manages on-device resource usage to support scalability without overwhelming hardware. We evaluate HCInfer on PC, smart home, and mobile platforms using OPT-13B, Qwen2.5-14B, and Llama2-13B models. Experiments show HCInfer achieves 1.67x to 4.3x speedup in TTFT and 1.16x to 17.15x speedup in TPOT compared to existing baselines, maintaining a sub-deadline miss rate (SubDMR) of 15.3% under worst-case conditions while keeping model accuracy degradation within 8% for typical cases and up to 11% in extreme scenarios. These results demonstrate HCInfer's potential to enable efficient and responsive LLM inference in real-world edge environments.
As industrial control applications scale up, mesh networked control systems (MNCSs) are gaining popularity, where multiple control loops share a multi-hop mesh network. However, these loops often suffer from long-span, time-varying delays caused by the multi-hop transmissions of multiple flows, which significantly degrade control performance, particularly stability. Existing studies on delay-aware stability conditions are usually independent with network scheduling, leading to a pessimistic stability analysis. Meanwhile, existing stability-aware scheduling approaches rely on coarse-grained designs, further worsening stability guarantees and limiting network capacity. In this work, we propose a stability-guaranteed scheduling mechanism for MNCSs with fine-grained timing. We first establish a stability condition that accounts for time-varying delays over an extended horizon spanning multiple superframes, which reduces the pessimism in stability analysis and enables more refined scheduling strategies. Based on this condition, We design a Long time-horizon and Fine-grained network scheduling mechanism with Stability Guarantee (LFSG), which deterministically maps the stability condition into the fine-grained network scheduling, considering fluctuating delays over the extended horizon. Furthermore, we provide a stability-capacity-aware LFSG (SCA-LFSG), which aims to maximize the number of stabilizable control loops and demonstrates its effectiveness through various application paradigms. Extensive studies demonstrate the advantages of stability analyses, LFSG, and SCA-LFSG over state-of-the-art approaches in terms of both control and timing performance.
Weakly-hard real-time systems enhance resource efficiency by allowing bounded deadline misses, but practical support in existing platforms remains limited. In this work-in-progress, we present the CBS-based Weakly-Hard Framework, which maps tasks with $(m, K)$ constraints into Constant Bandwidth Server (CBS) parameters under the SCHED_DEADLINE policy in Linux. The proposed approach requires no kernel modifications and guarantees schedulability through constrained-deadline EDF analysis. We implemented the framework as a user-space API and evaluated it on a Raspberry Pi platform. Experimental results show improved schedulability compared to existing methods and demonstrate potential opportunities for Quality-of-Service (QoS) enhancement.
