Real-Time Scheduling of DAG Tasks with Arbitrary Deadlines

Real-time scheduling and analysis of parallel tasks modeled as directed acyclic graphs (DAG) have been intensively studied in recent years. However, no existing work has explored the execution order of eligible vertices within a DAG task. In this paper, we show that this intra-task vertex execution order has a large impact on system schedulability and propose to control the execution order by vertex-level priority assignment. We develop analysis techniques to bound the worst-case response time for the proposed scheduling strategy and design heuristics for proper priority assignment to improve system schedulability as much as possible. We further extend the proposed approach to the general setting of multiple recurrent DAG tasks. Experiments with both realistic parallel benchmark applications and randomly generated workload show that our method consistently outperforms state-of-the-art methods with different task graph structures and parameter configurations.

Real-time scheduling and analysis of parallel tasks modeled as directed acyclic graphs (DAG) have been intensively studied in recent years. The degree of parallelism of DAG tasks is an important characterization in scheduling. This paper revisits the definition and the computing algorithms for the degree of parallelism of DAG tasks, and clarifies some misunderstandings regarding the degree of parallelism which exist in real-time literature. Based on the degree of the parallelism, we propose a real-time scheduling approach for DAG tasks, which is quite simple but rather effective and outperforms the state-of-the-art by a considerable margin.

The scheduling of parallel real-time tasks on multiprocessor systems is more complicated than the one of independent sequential tasks, specially for the Directed Acyclic Graph (DAG) model. The complexity is due to the structure of DAG tasks and the precedence constraints between their subtasks. The trivial DAG scheduling approach is to directly apply common real-time scheduling algorithms on DAGs despite their lack of compatibility with the parallel model. Another scheduling approach, which is called the stretching method, aims at transforming each parallel DAG task in the set into a collection of independent sequential threads that are easier to be scheduled. In this paper, we are interested in analyzing global preemptive scheduling of DAGs using both approaches by showing that they are not comparable when associated with Deadline Monotonic (DM) and Earliest Deadline First (EDF) scheduling algorithms. Then we use extensive simulations to evaluate their schedulability performance. To this end, we use our simulation tool YARTISS to generate random DAG tasks with many parameter variations so as to guarantee reliable experimental results.

With the rapid development of parallel programming techniques and the widespread use of multiprocessors, scheduling and analysis techniques supporting parallel real-time tasks become a critical topic for multiprocessor real-time systems. Global scheduling that allows the vertices of a parallel task to execute on any processor is a promising scheduling approach with guaranteed theoretical bounds and wide use in practice. However, the complex internal structure of parallel tasks leads to extensive inner- and inter-task interference, which leads to significant pessimism in the worst-case timing analysis. In this paper, a global fixed-priority (G-FP) scheduling with constrained parallelism for parallel real-time tasks based on the sporadic directed acyclic graph (DAG) model is proposed. Each DAG task is assigned a parallel threshold, such that the number of processors occupied by the task is limited to the parallel threshold of the task at a time. We propose a heuristic algorithm to set the parallel threshold and present a response-time analysis (RTA) to exploit the feature of the constrained parallel scheduling. Experiments with randomly generated tasks show that the proposed approach improves the schedulability upon G-FP and federated scheduling in terms of acceptance ratio.

Real-Time scheduling and analysis of parallel tasks on heterogeneous multi-cores

Energy harvesting sensors move into the mainstream because they are regarded as a perfect match for the monitoring applications. Nonetheless, their implementation is only feasible if the computing unit, generally a microcontroller, is capable of running the real-time tasks using sophisticated scheduling and power management techniques. In this paper, we address the hard real-time scheduling problem of Directed Acyclic Graph (DAG) tasks in a uniprocessor RTEH (Real Time Energy Harvesting) computing system. In this model, a task is defined as a set of dependent sub-tasks that execute under precedence constraints with processing time and energy requirements. We demonstrate that the energy aware ED-H algorithm provides an optimal solution. We show that the scheduling policy directly includes a feasibility analysis by enforcing the precedence constraints, thus making it possible to get rid of semaphore synchronization.

Global scheduling of real time tasks with precedence constraints has received significant attention recently. Specifically, on multiprocessor systems, the directed acyclic graph (DAG) model well-represents parallelizable workloads with precedence constraints. Hence, many studies have recently been published analyzing DAG structures using decomposition and window-based analysis methods. We identified a set of schedulable DAG taskets that are hard-to-analyze using state-of-the-art window-based schedulability tests. Additionally, we observe that the window-based test solely depends on one structural feature of the DAG taskset, which raises concerns about its pessimism in many settings. In this paper, to address these concerns, for hard real-time sporadic implicit-deadline DAG tasksets, we perform LAG-based schedulability analysis, which offers a more holistic view of the taskset than the window-based analysis. We present a companion utilization-based schedulability test to the state-of-the-art, which considers additional structural features of DAGs. Our results show that by considering such features, our LAG-based test empirically dominates the state-of-the-art test on over 80% of the evaluated DAG tasksets. Moreover, combining our LAG-based test in conjunction with the window-based tests can achieve high schedulability in many cases.

A model has been proposed in [Baruah et al., in Proceedings of the IEEE\nReal-Time Systems Symposium 2012] for representing recurrent\nprecedence-constrained tasks to be executed on multiprocessor platforms, where\neach recurrent task is modeled by a directed acyclic graph (DAG), a period, and\na relative deadline. Each vertex of the DAG represents a sequential job, while\nthe edges of the DAG represent precedence constraints between these jobs. All\nthe jobs of the DAG are released simultaneously and have to be completed within\nsome specified relative deadline. The task may release jobs in this manner an\nunbounded number of times, with successive releases occurring at least the\nspecified period apart. The feasibility problem is to determine whether such a\nrecurrent task can be scheduled to always meet all deadlines on a specified\nnumber of dedicated processors.\n The case of a single task has been considered in [Baruah et al., 2012]. The\nmain contribution of this paper is to consider the case of multiple tasks. We\nshow that EDF has a speedup bound of 2-1/m, where m is the number of\nprocessors. Moreover, we present polynomial and pseudopolynomial schedulability\ntests, of differing effectiveness, for determining whether a set of sporadic\nDAG tasks can be scheduled by EDF to meet all deadlines on a specified number\nof processors.\n

Parallel scheduling of task graphs has been an important research topic in the field of parallel processing, and it is well known to be a challenging NP-complete problem. Scheduling in data flow systems can be represented by a directed acyclic graph (DAG). In parallel task scheduling using DAG, managing the dependent tasks reduce overall execution time. However, the algorithm cannot be applied to real-time systems with a mix of different deadlines and multi outputs, such as an autonomous driving system, because real-time systems can be represented by a DAG are mainly limited threads known as a fork-join structure. Real-time scheduling algorithm that does not depend on the structure of DAG can be widely used. This paper presents the Heterogeneous Laxity-Based Scheduling (HLBS) algorithm, which uses list scheduling heuristics to solve this problem. HLBS computes the rest time until deadlines, known as laxity, and preferentially assigns a task with shorter laxity to the processor. This enables scheduling of multiple deadlines to reduce deadline miss rate. The evaluation results demonstrate that HLBS reduced the deadline miss rate by an average of 45.6 % while achieving a performance level comparable to that of algorithms presented in previous studies.

In this paper, aiming at a high miss deadline ratio in the large-scale task scheduling of heterogeneous parallel computing environment, the Arrived-Heterogeneous Earliest Finish Time (A-HEFT) algorithm based on the valid main path was proposed. This algorithm not only ensures all subtasks effectively processed within the time limit, but performs better in scheduling random arrival Directed Acyclic Graph (DAG) and the resource utilization among them during the execution period. Firstly, we proposed a new model of tasks produced by the same data source with a certain order based on the traditional model of DAG. Then, change the calculation of the upward rank of tasks in Heterogeneous Earliest Finish Time (HEFT) according to the task attribute of DAG, and get the execution priority from upward rank of sub-tasks. Finally, execute stretching scheduling based on idle time on the main path. Compared with Limited Preemptive Earliest Deadline First (LPEDF) algorithm and HEFT algorithm, the results of our work shows that A-HEFT algorithm performs better in processor utilization and success rate of task scheduling in heterogeneous system scheduling.

In prior work on soft real-time (SRT) multiprocessor scheduling, tardiness bounds have been derived for a variety of scheduling algorithms, most notably, the global earliest-deadline-first (G-EDF) algorithm. In this paper, we devise G-EDF-like (GEL) schedulers, which have identical implementations to G-EDF and therefore the same overheads, but that provide better tardiness bounds. We discuss how to analyze these schedulers and propose methods to determine scheduler parameters to meet several different tardiness bound criteria. We employ linear programs to adjust such parameters to optimize arbitrary tardiness criteria, and to analyze lateness bounds (lateness is related to tardiness). We also propose a particular scheduling algorithm, namely the global fair lateness (G-FL) algorithm, to minimize maximum absolute lateness bounds. Unlike the other schedulers described in this paper, G-FL only requires linear programming for analysis. We argue that our proposed schedulers, such as G-FL, should replace G-EDF for SRT applications.

Cloud computing systems often have two conflicting objective, maximizing service performance, and minimizing computing cost. The excellent task scheduling and resource allocation strategies can improve the cost/utility ratio efficiently. It is an NP-hard problem to optimize task scheduling of precedence-constrained parallel tasks represented by a directed acyclic graph (DAG) on the cloud system. In order to address this problem, a chemical reaction multi-objective optimization algorithm (CRMO) is proposed in this paper. The CRMO executes four chemical reaction operators (named on-wall ineffective collision, inter-molecular ineffective collision, decomposition, and synthesis) for cloud tasks DAG scheduling. The experimental results show that CRMO can produce outstanding cloud task scheduling solutions set.

We address the problem of schedulability analysis for a set of sporadic real-time tasks scheduled by the Global Earliest Deadline First (G-EDF) policy on a multiprocessor platform. State-of-the-art tests for schedulability analysis of multiprocessor global scheduling are often incomparable. That is, a task set that is judged not schedulable by a test may be verified to be schedulable by another test, and vice versa. In this paper, we first develop a new schedulability test that integrates the limited carry-in technique and Response Time Analysis (RTA) procedure for Global EDF schedulability analysis. Then, we provide an over-approximate variant of this test with better run-time efficiency. Later, we extend these two tests to self-suspending tasks. All schedulability tests proposed in the paper have provable dominance over their state-of-the-art counterparts. Finally, we conduct extensive comparisons among different schedulability tests. Our new tests show significant improvements for schedulability analysis of Global EDF.

The scheduling of tasks with deadlines on clusters is a key issue for offering quality-of-service (QoS) assurance. A critical challenge in real-time task scheduling is to handle various types of applications. This paper investigates the scheduling problem for processing a set of tasks comprising both divisible and indivisible real-time tasks on cluster systems. Indivisible tasks are characterized by the property that they need to be processed on their entirety on a single processor while divisible tasks can be distributed across several processing nodes by exploiting the underlying data parallelism. We propose a dynamic (on-line) real-time scheduling algorithm referred to as Hybrid Loads Push-Pull Scheduling (HLPPS) algorithm for handling a set of tasks comprising both divisible and indivisible real-time tasks on cluster systems. HLPPS is shown to efficiently exploit the parallelism in divisible tasks without undermining the schedulability of indivisible tasks and thereby optimize the overall performance. We consider two distinct network platforms - tightly coupled and loosely coupled clusters in designing the strategy. We conduct extensive performance evaluation studies to quantify the performance of the proposed algorithm under a variety of scenarios.

The Hierarchical Real-Time Scheduling (HiRTS) technique helps improve overall resource utilization in real-time embedded systems. With HiRTS, a computation resource is divided into a group of temporal resource partitions, each of which accommodates multiple real-time tasks. Besides the computation resource partitioning problem, real-time task scheduling on resource partitions is also a major problem of HiRTS. The existing scheduling techniques for dedicated resources, like schedulability tests and utilization bounds, are unable to work without changes on temporal resource partitions in most cases. In this paper, we show how to achieve maximal transparency for task scheduling on Regular Partitions, a type of resource partition introduced by the Regularity-based Resource Partition (RRP) Model. We show that several classes of real-time scheduling problems on a regular partition can be transformed into equivalent problems on a dedicated single resource, such that comprehensive single-resource scheduling techniques provide optimal solutions. Furthermore, this transformation method could be applied to different types of real-time tasks such as periodic tasks, sporadic tasks and aperiodic tasks.