Reservation-Based Federated Scheduling for Parallel Real-Time Tasks

This paper considers the scheduling of parallel real-time tasks with arbitrary-deadlines. Each job of a parallel task is described as a directed acyclic graph (DAG). In contrast to prior work in this area, where decomposition-based scheduling algorithms are proposed based on the DAG-structure and inter-task interference is analyzed as self-suspending behavior, this paper generalizes the federated scheduling approach. We propose a reservation-based algorithm, called reservation-based federated scheduling, that dominates federated scheduling. We provide general constraints for the design of such systems and prove that reservation-based federated scheduling has a constant speedup factor with respect to any optimal DAG task scheduler. Furthermore, the presented algorithm can be used in conjunction with any scheduler and scheduling analysis suitable for ordinary arbitrary-deadline sporadic task sets, i.e., without parallelism

Energy-Efficient Multi-Core Scheduling for Real-Time DAG Tasks

In this work, we study energy-aware real-time scheduling of a set of sporadic Directed Acyclic Graph (DAG) tasks with implicit deadlines. While meeting all real-time constraints, we try to identify the best task allocation and execution pattern such that the average power consumption of the whole platform is minimized. To the best of our knowledge, this is the first work that addresses the power consumption issue in scheduling multiple DAG tasks on multi-cores and allows intra-task processor sharing. We first adapt the decomposition-based framework for federated scheduling and propose an energy-sub-optimal scheduler. Then we derive an approximation algorithm to identify processors to be merged together for further improvements in energy-efficiency and to prove the bound of the approximation ratio. We perform a simulation study to demonstrate the effectiveness and efficiency of the proposed scheduling. The simulation results show that our algorithms achieve an energy saving of 27% to 41% compared to existing DAG task schedulers

Scheduling techniques to improve the worst-case execution time of real-time parallel applications on heterogeneous platforms

The key to providing high performance and energy-efficient execution for hard real-time applications is the time predictable and efficient usage of heterogeneous multiprocessors. However, schedulability analysis of parallel applications executed on unrelated heterogeneous multiprocessors is challenging and has not been investigated adequately by earlier works. The unrelated model is suitable to represent many of the multiprocessor platforms available today because a task (i.e., sequential code) may exhibit a different work-case-execution-time (WCET) on each type of processor on an unrelated heterogeneous multiprocessors platform. A parallel application can be realistically modeled as a directed acyclic graph (DAG), where the nodes are sequential tasks and the edges are dependencies among the tasks. This thesis considers a sporadic DAG model which is used broadly to analyze and verify the real-time requirements of parallel applications. A global work-conserving scheduler can efficiently utilize an unrelated platform by executing the tasks of a DAG on different processor types. However, it is challenging to compute an upper bound on the worst-case schedule length of the DAG, called makespan, which is used to verify whether the deadline of a DAG is met or not. There are two main challenges. First, because of the heterogeneity of the processors, the WCET for each task of the DAG depends on which processor the task is executing on during actual runtime. Second, timing anomalies are the main obstacle to compute the makespan even for the simpler case when all the processors are of the same type, i.e., homogeneous multiprocessors. To that end, this thesis addresses the following problem: How we can schedule multiple sporadic DAGs on unrelated multiprocessors such that all the DAGs meet their deadlines. Initially, the thesis focuses on homogeneous multiprocessors that is a special case of unrelated multiprocessors to understand and tackle the main challenge of timing anomalies. A novel timing-anomaly-free scheduler is proposed which can be used to compute the makespan of a DAG just by simulating the execution of the tasks based on this proposed scheduler. A set of representative task-based parallel OpenMP applications from the BOTS benchmark suite are modeled as DAGs to investigate the timing behavior of real-world applications. A simulation framework is developed to evaluate the proposed method. Furthermore, the thesis targets unrelated multiprocessors and proposes a global scheduler to execute the tasks of a single DAG to an unrelated multiprocessors platform. Based on the proposed scheduler, methods to compute the makespan of a single DAG are introduced. A set of representative parallel applications from the BOTS benchmark suite are modeled as DAGs that execute on unrelated multiprocessors. Furthermore, synthetic DAGs are generated to examine additional structures of parallel applications and various platform capabilities. A simulation framework that simulates the execution of the tasks of a DAG on an unrelated multiprocessor platform is introduced to assess the effectiveness of the proposed makespan computations. Finally, based on the makespan computation of a single DAG this thesis presents the design and schedulability analysis of global and federated scheduling of sporadic DAGs that execute on unrelated multiprocessors

A Measurement-Based Model for Parallel Real-Time Tasks

Under the federated paradigm of multiprocessor scheduling, a set of processors is reserved for the exclusive use of each real-time task. If tasks are characterized very conservatively (as is typical in safety-critical systems), it is likely that most invocations of the task will have computational demand far below the worst-case characterization, and could have been scheduled correctly upon far fewer processors than were assigned to it assuming the worst-case characterization of its run-time behavior. Provided we could safely determine during run-time when all the processors are going to be needed, for the rest of the time the unneeded processors could be idled in low-energy "sleep" mode, or used for executing non-real time work in the background. In this paper we propose a model for representing parallelizable real-time tasks in a manner that permits us to do so. Our model does not require us to have fine-grained knowledge of the internal structure of the code represented by the task; rather, it characterizes each task by a few parameters that are obtained by repeatedly executing the code under different conditions and measuring the run-times

A C-DAG task model for scheduling complex real-time tasks on heterogeneous platforms: preemption matters

Recent commercial hardware platforms for embedded real-time systems feature heterogeneous processing units and computing accelerators on the same System-on-Chip. When designing complex real-time application for such architectures, the designer needs to make a number of difficult choices: on which processor should a certain task be implemented? Should a component be implemented in parallel or sequentially? These choices may have a great impact on feasibility, as the difference in the processor internal architectures impact on the tasks' execution time and preemption cost. To help the designer explore the wide space of design choices and tune the scheduling parameters, in this paper we propose a novel real-time application model, called C-DAG, specifically conceived for heterogeneous platforms. A C-DAG allows to specify alternative implementations of the same component of an application for different processing engines to be selected off-line, as well as conditional branches to model if-then-else statements to be selected at run-time. We also propose a schedulability analysis for the C-DAG model and a heuristic allocation algorithm so that all deadlines are respected. Our analysis takes into account the cost of preempting a task, which can be non-negligible on certain processors. We demonstrate the effectiveness of our approach on a large set of synthetic experiments by comparing with state of the art algorithms in the literature

Response Time Bounds for DAG Tasks with Arbitrary Intra-Task Priority Assignment

Most parallel real-time applications can be modeled as directed acyclic graph (DAG) tasks. Intra-task priority assignment can reduce the nondeterminism of runtime behavior of DAG tasks, possibly resulting in a smaller worst-case response time. However, intra-task priority assignment incurs dependencies between different parts of the graph, making it a challenging problem to compute the response time bound. Existing work on intra-task task priority assignment for DAG tasks is subject to the constraint that priority assignment must comply with the topological order of the graph, so that the response time bound can be computed in polynomial time. In this paper, we relax this constraint and propose a new method to compute response time bound of DAG tasks with arbitrary priority assignment. With the benefit of our new method, we present a simple but effective priority assignment policy, leading to smaller response time bounds. Comprehensive evaluation with both single-DAG systems and multi-DAG systems demonstrates that our method outperforms the state-of-the-art method with a considerable margin