Directed Acyclic Graph Scheduling for Mixed-Criticality Systems

Many algorithms have recently been studied for scheduling mixed-criticality (MC) tasks. However, most existing MC scheduling algorithms guarantee the timely executions of high-criticality (HC) tasks at the expense of discarding low-criticality (LC) tasks, which can cause serious service interruption for such tasks. In this work, aiming at providing guaranteed services for LC tasks, we study an elastic mixed-criticality (E-MC) task model for dual-criticality systems. Specifically, the model allows each LC task to specify its maximum period (i.e., minimum service level) and a set of early-release points. We propose an early-release (ER) mechanism that enables LC tasks to be released more frequently and thus improve their service levels at runtime, with both conservative and aggressive approaches to exploiting system slack being considered, which is applied to both earliest deadline first (EDF) and preference-oriented earliest-deadline schedulers. We formally prove the correctness of the proposed early-release--earliest deadline first scheduler on guaranteeing the timeliness of all tasks through judicious management of the early releases of LC tasks. The proposed model and schedulers are evaluated through extensive simulations. The results show that by moderately relaxing the service requirements of LC tasks in MC task sets (i.e., by having LC tasks’ maximum periods in the E-MC model be two to three times their desired MC periods), most transformed E-MC task sets can be successfully scheduled without sacrificing the timeliness of HC tasks. Moreover, with the proposed ER mechanism, the runtime performance of tasks (e.g., execution frequencies of LC tasks, response times, and jitters of HC tasks) can be significantly improved under the ER schedulers when compared to that of the state-of-the-art earliest deadline first—virtual deadline scheduler.

Safety-critical cyber-physical systems like autonomous cars require not only different levels of assurance, but also close interactions with dynamically-changing physical environments. While the former has been studied extensively by exploiting the notion of mixed-criticality (MC) systems, the latter has not, especially in conjunction with MC systems. To fill this important gap, we conduct an in-depth case study, demonstrating the importance of capturing current physical states, and introduce the problem of achieving efficient utilization of computing resources under varying physical states in MC systems. To solve this problem, we first develop a physical-state-aware MC task model, which is a generalization of the existing basic MC task model. We then propose new slack concepts tailored to the new task model, which enable efficient utilization of computing resources for MC systems. Finally, we develop a physical-state-aware dynamic slack management framework and demonstrate how to utilize the new MC task model and slack concepts towards efficient system utilization. We show, via a case study and in-depth evaluation, that the proposed framework makes 20x less low-criticality jobs dropped over a popular MC scheduling algorithm without compromising the MC-schedulability requirements.

Mixed-criticality (MC) system has attracted a lot of research attention in the past few years for its resource efficiency. Recent work attempted to provide a new MC model, the so-called Flexible Mixed-Criticality (FMC) task model, to relax the pessimistic assumptions in classic MC scheduling. However, in FMC, the behavior of MC tasks is still analyzed in offline stage. The run-time behavior such as dynamic slack has not yet been studied in FMC scheduling framework. In this paper, we present a utilization-based slack scheduling framework for FMC tasks. In particular, we monitor task execution on run time and collect dynamic slacks generated by task early completion. And these slacks can then be used either by high-criticality tasks to reduce mode-switches, or by low-criticality tasks so that less suspensions are triggered with more execution time, and thus quality of service is improved. We evaluate our approach with extensive simulations, and experiment results demonstrate the effectiveness of the proposed approaches.

Directed acyclic graph (DAG) scheduling is a well-known problem, because a DAG can be used to describe a wide range of complex applications, including scientific applications and parallel computing jobs. Most DAG scheduling algorithms were proposed to minimize the job makespan (i.e., execution time) on a multiprocessor computer or cluster. However, as the cost-driven public cloud services have become an attractive and popular platform for providing computing resources, cost minimization emerges as a new critical issue. Therefore, the objective of this work is to propose and solve the cost optimization problem for scheduling DAGs on an IaaS cloud platform where task scheduling must cope with resource provisioning to achieve the optimal solution. In this paper, we proposed both optimal and heuristic scheduling algorithms, and we evaluated them across a variety of DAGs using the price model from EC2. Comparing to other cost-oblivious DAG schedules that aim to minimize makespan or resource usage, the results show that our cost-aware heuristic algorithm can reduce cost by 20---50 % and achieve a cost within x1.16 of the optimal one.

Mixed criticality systems are one of the relatively new directions of development for the classical real-time systems. As the real-time embedded systems become more and more complex, incorporating different tasks with different criticality levels, the continuous development of mixed criticality systems is only natural. These systems have practically entered every field where embedded systems are present: avionics, automotive, medical systems, wearable devices, home automation, industry and even the Internet of Things. While scheduling techniques have already been proposed in the literature for different types of mixed criticality systems, the number of papers addressing multiprocessor platforms running in a time-triggered mixed criticality environment is relatively low. These algorithms are easier to certify due to their complete determinism and isolation between components of different criticalities. Our research has centered on the problem of real-time scheduling on multiprocessor platforms for periodic tasks in a time-triggered mixed criticality environment. A partitioned, non-preemptive, table-driven scheduling algorithm was proposed, called Partitioned Time-Triggered Own Criticality Based Priority, based on a uniprocessor mixed criticality method. Furthermore, an analysis of the scheduling algorithm is provided in terms of success ratio by comparing it against an event-driven and a time-triggered method.

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.

Computational grids have recently attracted considerable attention as cost‐effective platforms for parallel processing. Generally, a parallel program carried out on such a platform can be characterized as a directed acyclic graph (DAG). To take full advantage of available resources in grids and achieve high computational performance, an effective DAG scheduling algorithm is indispensable. This study presents a novel constructive algorithm based on the Fast Ant System (FANT). The proposed algorithm, namely, the Fast Ant System for Task Matching and Scheduling (FANT‐TMS) algorithm, determines the scheduling order of all tasks in a parallel program and, then, allocates these tasks to appropriate processing elements in a computational grid, thereby minimizing total completion time of the parallel program. Performance of the FANTTMS algorithm is demonstrated by comparison with a genetic algorithm (GA)‐based scheduling technique in terms of overall schedule length of randomly generated DAGs. Experimental results demonstrate that the proposed algorithm outperforms the conventional GA‐based approach. Additionally, a novel local search strategy is devised that further enhances solution quality obtained with the FANT‐TMS algorithm.

Failure-resilient DAG task scheduling in edge computing

This paper proposes a directed acyclic graph (DAG) scheduling algorithm for cluster-based many-core architecture. Most of DAG scheduling methods that consider multiple processors and communication delays use a heuristic approach because it is difficult to shorten a schedule length (i.e.,makespan). Unfortunately, existing heuristic algorithms do not consider tasks that require a large number of computational resources. Such tasks typically need to be offloaded to a large-scale computational resource such as a many-core system or graphics processing unit (GPU). Therefore, we propose a DAG scheduling algorithm that uses a cluster-based many-core architecture to offload such computation; here, we use Kalray MPPA-256 as the many-core architecture. Our algorithm divides many-core computational resources for such tasks. Comparing our results with existing algorithms, we succeeded in shortening makespan with many types of DAGs while also considering Amdahl's law. We also compared the deadline miss ratio and execution time of our algorithm to existing algorithms, and show that the proposed algorithm is superior to the existing algorithms for any evaluation metrics.

With ever more complex functionalities being implemented in emerging real-time applications, multiprocessor systems are demanded for high performance, and directed acyclic graphs (DAGs) are used to model functional dependencies. In this work, we study a single periodic non-preemptive DAG running on a homogeneous multiprocessor platform, which is a common setup in many domains, such as automotive, robotics, and industrial automation. Aiming to reduce the makespan of the DAG and provide a tight yet safe bound, our contributions involve the exploitation of node-level parallelism and inter-node dependency, which are the two key factors of a DAG topology. First, we introduce a concurrent provider and consumer (CPC) model that precisely captures the above two factors, and can be recursively applied when parsing a DAG. Building upon CPC, we propose a novel scheduling method focused on reducing the makespan that orders the nodes in the following sequence: (i) the critical path, (ii) early predecessor paths of the critical path, and (iii) longer paths. Secondly, new response time analysis is presented, which provides a generic bound for any execution order of the non-critical nodes and a specific (tighter) bound for a fixed such order. Comprehensive evaluation demonstrates that our scheduling approach and analysis outperforms the state-of-the-art methods.

A scheduling algorithm based on reinforcement learning for heterogeneous environments

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.

Task Scheduling is one of the key elements in any distributed-memory machine (DMM), and an efficient algorithm can help reduce the interprocessor communication time. As optimal scheduling of tasks to DMMs is a strong NP-hard problem, many heuristic algorithms have been introduced in the literature. This paper presents a Task Duplication based Scheduling (TDS) algorithm which can schedule directed acyclic graphs (DAGs) with a complexity of O(|V|/sup 2/), where |V| is the number of tasks in the DAG. This algorithm generates an optimal schedule for a class of DAGs which satisfy a simple cost relationship. The performance of the algorithm has been observed by its application to some practical DAGs, and by comparing it with other existing scheduling schemes in terms of the schedule length and algorithm complexity.

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.

The traditional mixed-criticality (MC) model does not allow less critical tasks to execute during an event of the error and exception. Recently, the imprecise MC (IMC) model has been proposed where, even for exceptional events, less critical tasks also receive some amount of (degraded) service, e.g., a task overruns its execution demand. In this work, we present our ongoing effort to extend the IMC model to the precise scheduling of tasks and integrate with the dynamic voltage and frequency scaling (DVFS) scheme to enable energy minimization. Precise scheduling of MC systems is highly challenging because of its requirement to simultaneously guarantee the timing correctness of all tasks under both pessimistic and less pessimistic assumptions. We propose an utilization-based schedulability test and sufficient schedulability conditions for such systems under earliest deadline first with virtual deadline (EDF-VD) scheduling policy. For this unified model, we present a quantitative study in the forms of speedup bound and approximation ratio. Finally, both theoretical and experimental analysis will be conducted to prove the correctness of our algorithm and to demonstrate its effectiveness.