LAG-based Analysis Techniques for Scheduling Multiprocessor Hard Real-Time Sporadic DAGs

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.

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.

The scheduling of real-time tasks with primary-backup based fault-tolerant requirements has been an important problem for several years. Most of the known scheduling schemes are non-adaptive in nature meaning that they do not adapt to the dynamics of faults and task’s parameters in the system. In this paper,we propose an adaptive faulttolerant scheduling scheme that has a mechanism to control the overlap interval between the primary and backup versions of tasks such that the overall performance of the system is improved. The overlap interval is determined based on the estimated primary fault probability and task’s soft laxity. We also propose a new performance index that integrates schedulability (S) and reliability (R) into a single metric, called SR index. To evaluate the proposed scheme, we have conducted analytical and simulation studies under different fault and deadline scenarios, and found that the proposed adaptive scheme adapts to system dynamics and offers better SR index than that of the non-adaptive schemes.KeywordsSchedule SchemeProcessor UtilizationFault RateFault ProbabilityExecution IntervalThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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.

Real-time and embedded systems are shifting from single-core to multi-core processors, on which the software must be parallelized to fully utilize the computation capacity of the hardware. Recently, much work has been done on real-time scheduling of parallel tasks modeled as directed acyclic graphs (DAG). However, most of these studies assume tasks to have implicit or constrained deadlines. Much less work considered the general case of arbitrary deadlines (i.e., the relative deadline is allowed to be larger than the period), which is more difficult to analyze due to intra-task interference among jobs. In this article, we study the analysis of Global Earliest Deadline First (GEDF) scheduling for DAG parallel tasks with arbitrary deadlines. We develop new analysis techniques for GEDF scheduling of a single DAG task and this new analysis techniques can guarantee a better capacity augmentation bound 2.41 (the best known result is 2.5) in the case of a single task. Furthermore, the proposed analysis techniques are also extended to the case of multiple DAG tasks under GEDF and federated scheduling. Finally, through empirical evaluation, we justify the out-performance of our schedulability tests compared to the state-of-the-art in general.

In order to better solve the problem of heterogeneous multi-core processors with tasks and complex relationships to scheduling problem based on the directed acyclic graph (DAG) model, through the task replication and merge operation, designed using a parallel task tree (PTT) model. The model chooses ant colony optimization (ACO) with natural parallelism to improve the task migration to complete the replication and merge of tasks, so as to give full play to the advantages of heterogeneous multi-core processors, and schedule tasks to corresponding nuclear execution. Then the comparison and analysis of the PTT model and the DAG model in the same parameters are carried out by using the speedup ratio and the task execution time as the index. The experimental results show that the PTT model has a high speed-up ratio, shortens the task execution time, strengthens the stability, and verifies the rationality of the design.

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.

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

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.

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.

ABSTRACTCloud computing is a multifarious computing paradigm incorporating the benefits of service-oriented architecture and utility computing through virtualization. Hybrid cloud, an amalgamation of two or more public and/or private clouds, is gaining high popularity between users due to various reasons involving improved performance, flexible business operations, capacity expansion, optimized costs, and enhanced security. The efficient execution of fine-grained parallel applications onto hybrid cloud system becomes limited due to a number of factors. From the application point of view, it ranges from the dynamicity of the applications to their precedence and communication constraints while for the computational resources, it includes heterogeneity of processors and participating clouds with their interconnection topology. This work proposes a compile time hybrid cloud-based task duplication strategy to execute the fine-grained applications represented as directed acyclic graph (DAG) onto the hybrid cloud environment. The proposed strategy schedules the tasks based on a degree relative to the critical path in the DAG and tries to achieve lower bound of the DAG. Furthermore, it makes an effort to avoid redundant duplication by duplicating only the required parent tasks considering the available idle slots to minimize the execution time of the application. The experimental study reveals that the proposed strategy performs better than its peers in terms of achieving the lower bound more efficiently with lesser degree of duplication for fine-grained jobs. The strategy is highly useful for cloud environment as it results in lower cost of usage of resources with enhanced system utilization.

International audience

Access control is one of the most important means to protect the resources of multilevel security systems. So to make sure the authorization of multilevel security access control can be legally enforced, the access control policies must be correct and complete. Based on the implicit hierarchical relations between subjects and objects in multilevel security access control systems, this paper gives a method to build a unified directed acyclic graph(DAG) model (including both subjects and objects), using the partial orders based on sets. This method is easier to realize and is more utility for designing the access control model. Also based on partially ordered set, this paper proposes the algorithms for detecting policy conflicts and incomplete policies, which is embedding in the process of building DAG model. In the end, this paper verifies the correctness of the algorithm by experiment.