We address the problem of scheduling periodic implicit-deadline real-time tasks on m uniform processors. We introduce PCG^*, an optimal TL-plane algorithm based on PCG [Chen and Hsueh, 2008], which guarantees at most 2(m - 1) preemptions per TL-plane, matching the best-known theoretical bound for uniform platforms. The proposed algorithm advances the state of the art by offering an optimal real-time scheduling solution with a tight preemption bound within TL-planes. The numerical experiments presented in this work provide strong evidence that PCG^* yields a substantial reduction in the number of preemptions relative to PCG. When applied to identical processor platforms, PCG^* is also a best-possible polynomial time algorithm in terms of preemptions in a TL-plane, matching the (m-1) preemption bound achieved by LRE-TL [Funk, 2010].

This paper introduces unfair-PCG, the first unfair optimal scheduling algorithm for periodic implicit-deadline tasks on uniform multiprocessor platforms. The approach leverages the TL-plane scheduling method, allowing tasks to execute beyond their local execution time if processing resources are available. The algorithm ensures optimal resource utilisation while meeting task deadlines, thereby enhancing response times and enabling power-saving mechanisms.

Many cyber-physical systems are offloading computation-heavy programs to hardware accelerators (e.g., GPU and TPU) to reduce execution time. These applications will self-suspend between offloading data to the accelerators and obtaining the returned results. Previous efforts have shown that self-suspending tasks can cause scheduling anomalies, but none has examined inter-task communication. This paper aims to explore self-suspending tasks' data chain latency with periodic activation and asynchronous message passing. We first present the cause for suspension-induced delays and worst-case latency analysis. We then propose a rule for utilizing the hardware co-processors to reduce data chain latency and schedulability analysis. Simulation results show that the proposed strategy can improve overall latency while preserving system schedulability.

The seminal work on the global real-time scheduling of periodic tasks on unrelated multiprocessor platforms is based on a two-step method. First, the workload of each task is distributed over the processors and it is proved that this first step success ensures the existence of a feasible schedule. Then, using this workload assignment as an input, a template schedule construction method is presented. In this work, we review the seminal work and show by using a counter-example that this second step is incomplete. Thus, we propose and prove correct a novel and efficient algorithm to build the template schedule.

In safety-critical real-time systems domain, obtaining the appropriate operational model which meets the temporal (e.g. deadlines) and business (e.g. redundancy) requirements while being optimal in terms of several metrics is a primordial process in the design life-cycle. Recently, several researches have proposed to explore cross-domain trade-offs for a higher behaviour performance. Indeed, this process represents the first step in the deployment phase, which is very sensitive because it could be error-prone and time consuming.This paper is a work in progress proposing an approach aiming to help real-time system architects to take benefit from existing works, overcome their limits, and capitalize the efforts. Furthermore, the approach is based on the model-driven engineering paradigm and suggests to ease the usage of methods and tools thanks to repositories gathering them as a sort of a shared knowledge.

A data chain is a sequence of periodic realtime communicating tasks that are processing the data from sensors up to actuators. It determines an order in which the tasks propagate data but not in which they are executed: inter-task communication and scheduling are independent. In this paper, we focus on the latency computation, considered as the time elapsed from getting the data from an input and processing it to an output of a data chain. We propose a method for the worst-case latency calculation of periodic tasks' data chains executed by a partitioned fixed-priority preemptive scheduler upon a multiprocessor platform. As far as we know, there is no such formal approach based on closed-form expression for communicating real-time tasks.

The authors propose to study the average response time distribution of tasks owing to the pessimism introduced by the analysis of the synchronous case and unsafe response time obtained by simulation. In this regard, the problem we address is twofold. First, we need to determine a relevant and safe probabilistic feasibility interval that is representative of the systems behaviour over its entire lifetime. Second, we need to compute the response time distribution of any job of a task within this feasibility interval in order to combine them into an average distribution. This is a complex problem due to the fact that job arrivals are variable as well as their execution times.