arXiv subjects

Selma Saidi

Publications and source records attributed to Selma Saidi.

6 recordsLinked to original sources

arXiv2026

Interferences within a certifiable design methodology for high-performance multi-core platforms

The adoption of high-performance multi-core platforms in avionics and automotive systems introduces significant challenges in ensuring predictable execution, primarily due to shared resource interferences. Many existing approaches study interference from a single angle-for example, through hardware-level analysis or by monitoring software execution. However, no single abstraction level is sufficient on its own. Hardware behavior, program structure, and system configuration all interact, and a complete view is needed to understand where interferences come from and how to reduce them. In this paper, we present a methodology that brings together several tools that operate at different abstraction levels. At the lowest level, PHYLOG provides a formal model of the hardware and identifies possible interference channels using micro-architectural transactions. At the program level, machine learning analysis locates the exact parts of the code that are most sensitive to shared-resource contention. At the compilation level, MLIR-based transformations use this information to reshape memory access patterns and reduce pressure on shared resources. Finally, at the system level, Linux cgroups enforce static execution constraints to prevent highly interfering tasks from running together. The goal of our approach is to reduce memory interference and improve the system's predictability, thereby easing the certification process of multi-core systems in safety-critical domains.

cs.DC↗

arXiv2025

Collaborative Trustworthiness for Good Decision Making in Autonomous Systems

Autonomous systems are becoming an integral part of many application domains, like in the mobility sector. However, ensuring their safe and correct behaviour in dynamic and complex environments remains a significant challenge, where systems should autonomously make decisions e.g., about manoeuvring. We propose in this paper a general collaborative approach for increasing the level of trustworthiness in the environment of operation and improve reliability and good decision making in autonomous system. In the presence of conflicting information, aggregation becomes a major issue for trustworthy decision making based on collaborative data sharing. Unlike classical approaches in the literature that rely on consensus or majority as aggregation rule, we exploit the fact that autonomous systems have different quality attributes like perception quality. We use this criteria to determine which autonomous systems are trustworthy and borrow concepts from social epistemology to define aggregation and propagation rules, used for automated decision making. We use Binary Decision Diagrams (BDDs) as formal models for beliefs aggregation and propagation, and formulate reduction rules to reduce the size of the BDDs and allow efficient computation structures for collaborative automated reasoning.

cs.AI↗

arXiv2024

Global Scheduling of Weakly-Hard Real-Time Tasks using Job-Level Priority Classes

Real-time systems are intrinsic components of many pivotal applications, such as self-driving vehicles, aerospace and defense systems. The trend in these applications is to incorporate multiple tasks onto fewer, more powerful hardware platforms, e.g., multi-core systems, mainly for reducing cost and power consumption. Many real-time tasks, like control tasks, can tolerate occasional deadline misses due to robust algorithms. These tasks can be modeled using the weakly-hard model. Literature shows that leveraging the weakly-hard model can relax the over-provisioning associated with designed real-time systems. However, a wide-range of the research focuses on single-core platforms. Therefore, we strive to extend the state-of-the-art of scheduling weakly-hard real-time tasks to multi-core platforms. We present a global job-level fixed priority scheduling algorithm together with its schedulability analysis. The scheduling algorithm leverages the tolerable continuous deadline misses to assigning priorities to jobs. The proposed analysis extends the Response Time Analysis (RTA) for global scheduling to test the schedulability of tasks. Hence, our analysis scales with the number of tasks and number of cores because, unlike literature, it depends neither on Integer Linear Programming nor reachability trees. Schedulability analyses show that the schedulability ratio is improved by 40% comparing to the global Rate Monotonic (RM) scheduling and up to 60% more than the global EDF scheduling, which are the state-of-the-art schedulers on the RTEMS real-time operating system. Our evaluation on industrial embedded multi-core platform running RTEMS shows that the scheduling overhead of our proposal does not exceed 60 Nanosecond.

cs.OS↗

arXiv2024

Summary Paper: Use Case on Building Collaborative Safe Autonomous Systems-A Robotdog for Guiding Visually Impaired People

This is a summary paper of a use case of a Robotdog dedicated to guide visually impaired people in complex environment like a smart intersection. In such scenarios, the Robotdog has to autonomously decide whether it is safe to cross the intersection or not in order to further guide the human. We leverage data sharing and collaboration between the Robotdog and other autonomous systems operating in the same environment. We propose a system architecture for autonomous systems through a separation of a collaborative decision layer, to enable collective decision making processes, where data about the environment, relevant to the Robotdog decision, together with evidences for trustworthiness about other systems and the environment are shared.

cs.RO↗

arXiv2023

Trace-enabled Timing Model Synthesis for ROS2-based Autonomous Applications

Autonomous applications are typically developed over Robot Operating System 2.0 (ROS2) even in time-critical systems like automotive. Recent years have seen increased interest in developing model-based timing analysis and schedule optimization approaches for ROS2-based applications. To complement these approaches, we propose a tracing and measurement framework to obtain timing models of ROS2-based applications. It offers a tracer based on extended Berkeley Packet Filter (eBPF) that probes different functions in ROS2 middleware and reads their arguments or return values to reason about the data flow in applications. It combines event traces from ROS2 and the operating system to generate a directed acyclic graph showing ROS2 callbacks, precedence relations between them, and their timing attributes. While being compatible with existing analyses, we also show how to model (i)~message synchronization, e.g., in sensor fusion, and (ii)~service requests from multiple clients, e.g., in motion planning. Considering that, in real-world scenarios, the application code might be confidential and formal models are unavailable, our framework still enables the application of existing analysis and optimization techniques.

cs.OS↗

arXiv2023

Collective Reasoning for Safe Autonomous Systems

Collaboration in multi-agent autonomous systems is critical to increase performance while ensuring safety. However, due to heterogeneity of their features in, e.g., perception qualities, some autonomous systems have to be considered more trustworthy than others when contributing to collaboratively build a common environmental model, especially under uncertainty. In this paper, we introduce the idea of increasing the reliability of autonomous systems by relying on collective intelligence. We borrow concepts from social epistemology to exploit individual characteristics of autonomous systems, and define and formalize at design rules for collective reasoning to achieve collaboratively increased safety, trustworthiness and good decision making.

cs.MA↗