U-EDF: An Unfair But Optimal Multiprocessor Scheduling Algorithm for Sporadic Tasks

A multiprocessor scheduling algorithm named U-EDF, was presented in [1] for the scheduling of periodic tasks with implicit deadlines. It was claimed that U-EDF is optimal for periodic tasks (i.e. it can meet all deadlines of every schedulable task set) and extensive simulations showed a drastic improvement in the number of task preemptions and migrations in comparison to state-of-the-art optimal algorithms. However, there was no proof of its optimality and U-EDF was not designed to schedule sporadic tasks. In this work, we propose a generalization of U-EDF for the scheduling of sporadic tasks with implicit deadlines, and we prove its optimality. Contrarily to all other existing optimal multiprocessor scheduling algorithms for sporadic tasks, U-EDF is not based on the fairness property. Instead, it extends the main principles of EDF so that it achieves optimality while benefiting from a substantial reduction in the number of preemptions and migrations. © 2012 IEEE.SCOPUS: cp.pinfo:eu-repo/semantics/publishe

An off-line multiprocessor real-time scheduling algorithm to reduce static energy consumption

International audienceEnergy consumption of highly reliable real-time embedded systems is a significant concern. Static energy consumption tends to become more important than dynamic energy consumption. This paper aims to propose a new off-line scheduling algorithm to put as much as possible processors in low- power states instead of idling. In these states, energy consumption is reduced, enhancing the battery life-time of mission critical systems. However, no instruction can be executed and a transition delay is required to come back to the active state. Activating deeper low-power states requires to produce larger idle periods. As the processor usage is constant for a given task set, this objective implies reducing the number of idle periods. Our proposal is to modelize the processors idle time as an additional task. Then we formalize the problem as a linear equation system with the objective of reducing the number of preemptions (or executions) of this additional task. Simulations show that our algorithm is more energy efficient than existing algorithms

An Evaluation of Adaptive Partitioning of Real-Time Workloads on Linux

This paper provides an open implementation and an experimental evaluation of an adaptive partitioning approach for scheduling real-time tasks on symmetric multicore systems. The proposed technique is based on combining partitioned EDF scheduling with an adaptive migration policy that moves tasks across processors only when strictly needed to respect their temporal constraints. The implementation of the technique within the Linux kernel, via modifications to the SCHED_DEADLINE code base, is presented. An extensive experimentation-has been conducted by applying the technique on a real multi-core platform with several randomly generated synthetic task sets. The obtained experimental results highlight that the approach exhibits a promising performance to schedule real-time workloads on a real system, with a greatly reduced number of migrations compared to the original global EDF available in SCHED_DEADLINE

Operating System Contribution to Composable Timing Behaviour in High-Integrity Real-Time Systems

The development of High-Integrity Real-Time Systems has a high footprint in terms of human, material and schedule costs. Factoring functional, reusable logic in the application favors incremental development and contains costs. Yet, achieving incrementality in the timing behavior is a much harder problem. Complex features at all levels of the execution stack, aimed to boost average-case performance, exhibit timing behavior highly dependent on execution history, which wrecks time composability and incrementaility with it. Our goal here is to restitute time composability to the execution stack, working bottom up across it. We first characterize time composability without making assumptions on the system architecture or the software deployment to it. Later, we focus on the role played by the real-time operating system in our pursuit. Initially we consider single-core processors and, becoming less permissive on the admissible hardware features, we devise solutions that restore a convincing degree of time composability. To show what can be done for real, we developed TiCOS, an ARINC-compliant kernel, and re-designed ORK+, a kernel for Ada Ravenscar runtimes. In that work, we added support for limited-preemption to ORK+, an absolute premiere in the landscape of real-word kernels. Our implementation allows resource sharing to co-exist with limited-preemptive scheduling, which extends state of the art. We then turn our attention to multicore architectures, first considering partitioned systems, for which we achieve results close to those obtained for single-core processors. Subsequently, we shy away from the over-provision of those systems and consider less restrictive uses of homogeneous multiprocessors, where the scheduling algorithm is key to high schedulable utilization. To that end we single out RUN, a promising baseline, and extend it to SPRINT, which supports sporadic task sets, hence matches real-world industrial needs better. To corroborate our results we present findings from real-world case studies from avionic industry

An Efficient Online Benefit-aware Multiprocessor Scheduling Technique for Soft Real-Time Tasks Using Online Choice of Approximation Algorithms

Maximizing the benefit gained by soft real-time tasks in many applications and embedded systems is highly needed to provide an acceptable QoS (Quality of Service). Examples of such applications and embedded systems include real-time medical monitoring systems, video- streaming servers, multiplayer video games, and mobile multimedia devices. In these systems, tasks are not equally critical (or beneficial). Each task comes with its own benefit-density function which can be different from the others’. The sooner a task completes, the more benefit it gains. In this work, a novel online benefit-aware preemptive approach is presented in order to enhance scheduling of soft real-time aperiodic and periodic tasks in multiprocessor systems. The objective of this work is enhancing the QoS by increasing the total benefit, while reducing flow times and deadline misses. This method prioritizes the tasks using their benefit-density functions, which imply their importance to the system, and schedules them in a real-time basis. The first model I propose is for scheduling soft real-time aperiodic tasks. An online choice of two approximation algorithms, greedy and load-balancing, is used in order to distribute the low- priority tasks among identical processors at the time of their arrival without using any statistics. The results of theoretical analysis and simulation experiments show that this method is able to maximize the gained benefit and decrease the computational complexity (compared to existing algorithms) while minimizing makespan with fewer missed deadlines and more balanced usage of processors. I also propose two more versions of this algorithm for scheduling SRT periodic tasks, with implicit and non-implicit deadlines, in addition to another version with a modified loadbalancing factor. The extensive simulation experiments and empirical comparison of these algorithms with the state of the art, using different utilization levels and various benefit density functions show that these new techniques outperform the existing ones. A general framework for benefit-aware multiprocessor scheduling in applications with periodic, aperiodic or mixed real-time tasks is also provided in this work.Computer Science, Department o

Thermal-aware real-time scheduling using timed continuous Petri Nets

We present a thermal-aware, hard real-time (HRT) global scheduler for a multiprocessor system designed upon three novel techinques. First, we present a modeling methodology based on Timed Continuous Petri nets (TCPN) that yields a complete state variable model, including job arrivals, CPU usage, power, and thermal behavior. The model is accurate and avoids the calibration stage of RC thermal models. Second, based on this model, a linear programming problem (LPP) determines the existence of a feasible HRT thermal-aware schedule. Last, a sliding-mode controller and an online discretization algorithm implement the global HRT scheduler, which is capable of managing thermal constraints, context switching, migrations, and disturbances

Scheduling and locking in multiprocessor real-time operating systems

With the widespread adoption of multicore architectures, multiprocessors are now a standard deployment platform for (soft) real-time applications. This dissertation addresses two questions fundamental to the design of multicore-ready real-time operating systems: (1) Which scheduling policies offer the greatest flexibility in satisfying temporal constraints; and (2) which locking algorithms should be used to avoid unpredictable delays? With regard to Question 1, LITMUSRT, a real-time extension of the Linux kernel, is presented and its design is discussed in detail. Notably, LITMUSRT implements link-based scheduling, a novel approach to controlling blocking due to non-preemptive sections. Each implemented scheduler (22 configurations in total) is evaluated under consideration of overheads on a 24-core Intel Xeon platform. The experiments show that partitioned earliest-deadline first (EDF) scheduling is generally preferable in a hard real-time setting, whereas global and clustered EDF scheduling are effective in a soft real-time setting. With regard to Question 2, real-time locking protocols are required to ensure that the maximum delay due to priority inversion can be bounded a priori. Several spinlock- and semaphore-based multiprocessor real-time locking protocols for mutual exclusion (mutex), reader-writer (RW) exclusion, and k-exclusion are proposed and analyzed. A new category of RW locks suited to worst-case analysis, termed phase-fair locks, is proposed and three efficient phase-fair spinlock implementations are provided (one with few atomic operations, one with low space requirements, and one with constant RMR complexity). Maximum priority-inversion blocking is proposed as a natural complexity measure for semaphore protocols. It is shown that there are two classes of schedulability analysis, namely suspension-oblivious and suspension-aware analysis, that yield two different lower bounds on blocking. Five asymptotically optimal locking protocols are designed and analyzed: a family of mutex, RW, and k-exclusion protocols for global, partitioned, and clustered scheduling that are asymptotically optimal in the suspension-oblivious case, and a mutex protocol for partitioned scheduling that is asymptotically optimal in the suspension-aware case. A LITMUSRT-based empirical evaluation is presented that shows these protocols to be practical

REAL-TIME SCHEDULING ON ASYMMETRIC MULTIPROCESSOR PLATFORMS

Real-time scheduling analysis is crucial for time-critical systems, in which provable timing guarantees are more important than observed raw performance. Techniques for real-time scheduling analysis initially targeted uniprocessor platforms but have since evolved to encompass multiprocessor platforms. However, work directed at multiprocessors has largely focused on symmetric platforms, in which every processor is identical. Today, it is common for a multiprocessor to include heterogeneous processing elements, as this offers advantages with respect to size, weight, and power (SWaP) limitations. As a result, realizing modern real-time systems on asymmetric multiprocessor platforms is an inevitable trend. Unfortunately, principles and mechanisms regarding real-time scheduling on such platforms are relatively lacking. The goal of this dissertation is to enrich such principles and mechanisms, by bridging existing analysis for symmetric multiprocessor platforms to asymmetric ones and by developing new techniques that are unique for asymmetric multiprocessor platforms. The specific contributions are threefold. First, for a platform consisting of processors that differ with respect to processing speeds only, this dissertation shows that the preemptive global earliest-deadline-first (G-EDF) scheduler is optimal for scheduling soft real-time (SRT) task systems. Furthermore, it shows that semi-partitioned scheduling, which is a hybrid of conventional global and partitioned scheduling approaches, can be applied to optimally schedule both hard real-time (HRT) and SRT task systems. Second, on platforms that consist of processors with different functionalities, tasks that belong to different functionalities may process the same source data consecutively and therefore have producer/consumer relationships among them, which are represented by directed acyclic graphs (DAGs). End-to-end response-time bounds for such DAGs are derived in this dissertation under a G-EDF-based scheduling approach, and it is shown that such bounds can be improved by a linear-programming-based deadline-setting technique. Third, processor virtualization can lead a symmetric physical platform to be asymmetric. In fact, for a designated virtual-platform capacity, there exist an infinite number of allocation schemes for virtual processors and a choice must be made. In this dissertation, a particular asymmetric virtual-processor allocation scheme, called minimum-parallelism (MP) form, is shown to dominate all other schemes including symmetric ones.Doctor of Philosoph