Response-Time Analysis of Conditional DAG Tasks in Multiprocessor Systems

Different task models have been proposed to represent the parallel structure of real-time tasks executing on manycore platforms: fork/join, synchronous parallel, DAG-based, etc. Despite different schedulability tests and resource augmentation bounds are available for these task systems, we experience difficulties in applying such results to real application scenarios, where the execution flow of parallel tasks is characterized by multiple (and nested) conditional structures. When a conditional branch drives the number and size of sub-jobs to spawn, it is hard to decide which execution path to select for modeling the worst-case scenario. To circumvent this problem, we integrate control flow information in the task model, considering conditional parallel tasks (cp-tasks) represented by DAGs composed of both precedence and conditional edges. For this task model, we identify meaningful parameters that characterize the schedulability of the system, and derive efficient algorithms to compute them. A response time analysis based on these parameters is then presented for different scheduling policies. A set of simulations shows that the proposed approach allows efficiently checking the schedulability of the addressed systems, and that it significantly tightens the schedulability analysis of non-conditional (e.g., Classic DAG) tasks over existing approaches

Response-Time Analysis of Limited-Preemptive Parallel DAG Tasks Under Global Scheduling

Most recurrent real-time applications can be modeled as a set of sequential code segments (or blocks) that must be (repeatedly) executed in a specific order. This paper provides a schedulability analysis for such systems modeled as a set of parallel DAG tasks executed under any limited-preemptive global job-level fixed priority scheduling policy. More precisely, we derive response-time bounds for a set of jobs subject to precedence constraints, release jitter, and execution-time uncertainty, which enables support for a wide variety of parallel, limited-preemptive execution models (e.g., periodic DAG tasks, transactional tasks, generalized multi-frame tasks, etc.). Our analysis explores the space of all possible schedules using a powerful new state abstraction and state-pruning technique. An empirical evaluation shows the analysis to identify between 10 to 90 percentage points more schedulable task sets than the state-of-the-art schedulability test for limited-preemptive sporadic DAG tasks. It scales to systems of up to 64 cores with 20 DAG tasks. Moreover, while our analysis is almost as accurate as the state-of-the-art exact schedulability test based on model checking (for sequential non-preemptive tasks), it is three orders of magnitude faster and hence capable of analyzing task sets with more than 60 tasks on 8 cores in a few seconds

On the Pitfalls of Resource Augmentation Factors and Utilization Bounds in Real-Time Scheduling

In this paper, we take a careful look at speedup factors, utilization bounds, and capacity augmentation bounds. These three metrics have been widely adopted in real-time scheduling research as the de facto standard theoretical tools for assessing scheduling algorithms and schedulability tests. Despite that, it is not always clear how researchers and designers should interpret or use these metrics. In studying this area, we found a number of surprising results, and related to them, ways in which the metrics may be misinterpreted or misunderstood. In this paper, we provide a perspective on the use of these metrics, guiding researchers on their meaning and interpretation, and helping to avoid pitfalls in their use. Finally, we propose and demonstrate the use of parametric augmentation functions as a means of providing nuanced information that may be more relevant in practical settings

An Analysis of Lazy and Eager Limited Preemption Approaches under DAG-Based Global Fixed Priority Scheduling

DAG-based scheduling models have been shown to effectively express the parallel execution of current many-core heterogeneous architectures. However, their applicability to real-time settings is limited by the difficulties to find tight estimations of the worst-case timing parameters of tasks that may arbitrarily be preempted/migrated at any instruction. An efficient approach to increase the system predictability is to limit task preemptions to a set of pre-defined points. This limited preemption model supports two different preemption approaches, eager and lazy, which have been analyzed only for sequential task-sets. This paper proposes a new response time analysis that computes an upper bound on the lower priority blocking that each task may incur with eager and lazy preemptions. We evaluate our analysis with both, synthetic DAG-based task-sets and a real case-study from the automotive domain. Results from the analysis demonstrate that, despite the eager approach generates a higher number of priority inversions, the blocking impact is generally smaller than in the lazy approach, leading to a better schedulability performance.This work was funded by the EU projects P-SOCRATES (FP7-ICT-2013-10) and HERCULES (H2020/ICT/2015/688860), and the Spanish Ministry of Science and Innovation under contract TIN2015-65316-P.Peer ReviewedPostprint (author's final draft

Global EDF scheduling of directed acyclic graphs on multiprocessor systems

International audienceIn this paper, we study the problem of real-time scheduling of parallel tasks represented by a Directed Acyclic Graph (DAG) on multiprocessor architectures. We focus on Global Earliest Deadline First scheduling of sporadic DAG tasksets with constrained-deadlines on a system of homogeneous processors. Our contributions consist in analyzing DAG tasks by considering their internal structures and providing a tighter bound on the workload and interference analysis. This approach consists in assigning a local offset and deadline for each subtask in the DAG. We derive an improved sufficient schedulability test w.r.t. an existing test proposed in the state of the art. Then we discuss the sustainability of this test

Analysis of Real-Time Capabilities of Dynamic Scheduled System

This PhD-thesis explores different real-time scheduling approaches to effectively utilize industrial real-time applications on multicore or manycore platforms. The proposed scheduling policy is named the Time-Triggered Constant Phase scheduler for handling periodic tasks, which determines time windows for each computation and communication in advance by using the dependent task model

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

Hard Real-Time Stationary GANG-Scheduling

The scheduling of parallel real-time tasks enables the efficient utilization of modern multiprocessor platforms for systems with real-time constrains. In this situation, the gang task model, in which each parallel sub-job has to be executed simultaneously, has shown significant performance benefits due to reduced context switches and more efficient intra-task synchronization. In this paper, we provide the first schedulability analysis for sporadic constrained-deadline gang task systems and propose a novel stationary gang scheduling algorithm. We show that the schedulability problem of gang task sets can be reduced to the uniprocessor self-suspension schedulability problem. Furthermore, we provide a class of partitioning algorithms to find a stationary gang assignment and show that it bounds the worst-case interference of each task. To demonstrate the effectiveness of our proposed approach, we evaluate it for implicit-deadline systems using randomized task sets under different settings, showing that our approach outperforms the state-of-the-art