Real-time scheduling algorithm for safety-critical systems on faulty multicore environments

An algorithm (called FTM) for scheduling of real-time sporadic tasks on a multicore platform is proposed. Each task has a deadline by which it must complete its non-erroneous execution. The FTM algorithm executes backups in order to recover from errors caused by non-permanent and permanent hardware faults. The worst-case schedulability analysis of FTM algorithm is presented considering an application-level error model, which is independent of the stochastic behavior of the underlying hardware-level fault model. Then, the stochastic behavior of hardware-level fault model is plugged in to the analysis to derive the probability of meeting all the deadlines. Such probabilistic guarantee is the level of assurance (i.e., reliability) regarding the correct functional and timing behaviors of the system. One of the salient features of FTM algorithm is that it executes some backups in active redundancy to exploit the parallel multicore architecture while other backups passively to avoid unnecessary execution of too many active backups. This paper also proposes a scheme to determine for each task the number of backups that should run in active redundancy in order to increase the probability of meeting all the deadlines. The effectiveness of the proposed approach is demonstrated using an example application.     

A fault-tolerant dynamic scheduling algorithm for multiprocessorreal-time systems and its analysis

Many time-critical applications require dynamic scheduling with predictable performance. Tasks corresponding to these applications have deadlines to be met despite the presence of faults. In this paper, we propose an algorithm to dynamically schedule arriving real-time tasks with resource and fault-tolerant requirements on to multiprocessor systems. The tasks are assumed to be nonpreemptable and each task has two copies (versions) which are mutually excluded in space, as well as in time in the schedule, to handle permanent processor failures and to obtain better performance, respectively. Our algorithm can tolerate more than one fault at a time, and employs performance improving techniques such as 1) distance concept which decides the relative position of the two copies of a task in the task queue, 2) flexible backup overloading, which introduces a trade-off between degree of fault tolerance and performance, and 3) resource reclaiming, which reclaims resources both from deallocated backups and early completing tasks. We quantify, through simulation studies, the effectiveness of each of these techniques in improving the guarantee ratio, which is defined as the percentage of total tasks, arrived in the system, whose deadlines are met. Also, we compare through simulation studies the performance our algorithm with a best known algorithm for the problem, and show analytically the importance of distance parameter in fault-tolerant dynamic scheduling in multiprocessor real-time systems     

Real-time task scheduling by multiobjective genetic algorithm

Real-time tasks are characterized by computational activities with timing constraints and classified into two categories: a hard real-time task and a soft real-time task. In hard real-time tasks, tardiness can be catastrophic. The goal of hard real-time tasks scheduling algorithms is to meet all tasks’ deadlines, in other words, to keep the feasibility of scheduling through admission control. However, in the case of soft real-time tasks, slight violation of deadlines is not so critical.In this paper, we propose a new scheduling algorithm for soft real-time tasks using multiobjective genetic algorithm (moGA) on multiprocessors system. It is assumed that tasks have precedence relations among them and are executed on homogeneous multiprocessor environment.The objective of the proposed scheduling algorithm is to minimize the total tardiness and total number of processors used. For these objectives, this paper combines adaptive weight approach (AWA) that utilizes some useful information from the current population to readjust weights for obtaining a search pressure toward a positive ideal point. The effectiveness of the proposed algorithm is shown through simulation studies.     

On the design of communication-aware fault-tolerant scheduling algorithms for precedence constrained tasks in grid computing systems with dedicated communication devices

Fault-tolerant scheduling is an imperative step for large-scale computational Grid systems, as often geographically distributed nodes co-operate to execute a task. By and large, primary-backup approach is a common methodology used for fault tolerance wherein each task has a primary and a backup on two different processors. In this paper, we address the problem of how to schedule DAGs in Grids with communication delays so that service failures can be avoided in the presence of processors faults. The challenge is, that as tasks in a DAG have dependence on each other, a task must be scheduled to make sure that it will succeed when any of its predecessors fails due to a processor failure. We first propose a communication model and determine when communications between a backup and backups of its successors are necessary. Then we determine when a backup can start and its eligible processors so as to guarantee that every DAG can complete upon any processor failure. We develop two algorithms to schedule backups, which minimize response time and replication cost, respectively. We also develop a suboptimal algorithm which targets minimizing replication cost while not affecting response time. We conduct extensive simulation experiments to quantify the performance of the proposed algorithms.     

On-line scheduling of scalable real-time tasks on multiprocessor systems

The computation time of scalable tasks depends on the number of processors allocated to them in multiprocessor systems. As more processors are allocated to a scalable task, the overall computation time of the task decreases but the total amount of processors’ time devoted to the execution of the task, called workload, increases due to parallel execution overhead. In this paper, we propose a task scheduling algorithm that utilizes the property of scalable tasks for on-line and real-time scheduling. In the proposed algorithm, the total workload of all scheduled tasks is reduced by managing processors allocated to the tasks as few as possible without missing their deadlines. As a result, the processors in the system have less load to execute the scheduled tasks and can execute more newly arriving tasks before their deadlines. Simulation results show that the proposed algorithm performs significantly better than the conventional algorithm based on a fixed number of processors to execute each task.     

Bounding and shaping the demand of generalized mixed-criticality sporadic task systems

We generalize the commonly used mixed-criticality sporadic task model to let all task parameters (execution-time, deadline and period) change between criticality modes. In addition, new tasks may be added in higher criticality modes and the modes may be arranged using any directed acyclic graph, where the nodes represent the different criticality modes and the edges the possible mode switches. We formulate demand bound functions for mixed-criticality sporadic tasks and use these to determine EDF-schedulability. Tasks have different demand bound functions for each criticality mode. We show how to shift execution demand between different criticality modes by tuning the relative deadlines. This allows us to shape the demand characteristics of each task. We propose efficient algorithms for tuning all relative deadlines of a task set in order to shape the total demand to the available supply of the computing platform. Experiments indicate that this approach is successful in practice. This new approach has the added benefit of supporting hierarchical scheduling frameworks.     

An analysis of the non-preemptive mixed-criticality match-up scheduling problem

Many applications have a mixed-criticality nature. They contain tasks with a different criticality, meaning that a task with a lower criticality can be skipped if a task with a higher criticality needs more time to be executed. This paper deals with a mixed-criticality scheduling problem where each task has a criticality given by a positive integer number. The exact processing time of the task is not known. Instead, we use different upper bounds of the processing time for different criticality levels of the schedule. A schedule with different criticality levels is generated off-line, but its on-line execution switches among the criticality levels depending on the actual values of the processing times. The advantage is that after the transient prolongation of a higher criticality task, the system is able to match up with the schedule on a lower criticality level. While using this model, we achieve significant schedule efficiency (assuming that the prolongation of the higher criticality task rarely occurs), and at the same time, we are able to grant a sufficient amount of time to higher criticality tasks (in such cases, some of the lower criticality tasks may be skipped). This paper shows a motivation for the non-preemptive mixed-criticality match-up scheduling problem arising from the area of the communication protocols. Using a polynomial reduction from the 3-partition problem, we prove the problem to be \(\mathcal {NP}\)-hard in the strong sense even when the release dates and deadlines are dropped and only two criticality levels are considered.     

Converging to periodic schedules for cyclic scheduling problems with resources and deadlines

Cyclic scheduling has been widely studied because of the importance of applications in manufacturing systems and in computer science. For this class of problems, a finite set of tasks with precedence relations and resource constraints must be executed repetitively while maximizing the throughput. Many applications also require that execution schedules be periodic i.e. the execution of each task is repeated with a fixed global period w.The present paper develops a new method to build periodic schedules with cumulative resource constraints, periodic release dates and deadlines. The main idea is to fix the period w, to unwind the cyclic scheduling problem for some number of iterations, and to add precedence relations so that the minimum time lag between two successive executions of any task equals w. Then, using any usual (not cyclic) scheduling algorithm to compute task starting times for the unwound problem, we prove that either the method converges to a periodic schedule of period w or it fails to compute a schedule. A non-polynomial upper bound on the number of iterations to unwind in order to guarantee that cyclic precedence relations and resource constraints are fulfilled is also provided. This method is successfully applied to a real-life problem, namely the software pipelining of inner loops on an embedded VLIW processor core by using a Graham list scheduling algorithm.     

High performance real-time scheduling of multiple mixed-criticality functions in heterogeneous distributed embedded systems

The architectures of high-end embedded system have evolved into heterogeneous distributed integrated architectures. The scheduling of multiple distributed mixed-criticality functions in heterogeneous distributed embedded systems is a considerable challenge because of the different requirements of systems and functions. Overall scheduling length (i.e., makespan) is the main concern in system performance, whereas deadlines represent the major timing constraints of functions. Most algorithms use the fairness policies to reduce the makespan in heterogeneous distributed systems. However, these fairness policies cannot meet the deadlines of most functions. Each function has different criticality levels (e.g., severity), and missing the deadlines of certain high-criticality functions may cause fatal injuries to people under this situation. This study first constructs related models for heterogeneous distributed embedded systems. Thereafter, the criticality certification, scheduling framework, and fairness of multiple heterogeneous earliest finish time (F_MHEFT) algorithm for heterogeneous distributed embedded systems are presented. Finally, this study proposes a novel algorithm called the deadline-span of multiple heterogeneous earliest finish time (D_MHEFT), which is a scheduling algorithm for multiple mixed-criticality functions. The F_MHEFT algorithm aims at improving the performance of systems, while the D_MHEFT algorithm tries to meet the deadlines of more high-criticality functions by sacrificing a certain performance. The experimental results demonstrate that the D_MHEFT algorithm can significantly reduce the deadline miss ratio (DMR) and keep satisfactory performance over existing methods.     

An optimal boundary fair scheduling algorithm for multiprocessor real-time systems

With the emergence of multicore processors, the research on multiprocessor real-time scheduling has caught more researchers’ attention recently. Although the topic has been studied for decades, it is still an evolving research field with many open problems. In this work, focusing on periodic real-time tasks with quantum-based computation requirements and implicit deadlines, we propose a novel optimal scheduling algorithm, namely boundary fair (Bfair), which can achieve full system utilization as the well-known Pfair scheduling algorithms. However, different from Pfair algorithms that make scheduling decisions and enforce proportional progress (i.e., fairness) for all tasks at each and every time unit, Bfair makes scheduling decisions and enforces fairness to tasks only at tasks’ period boundaries (i.e., deadlines of periodic tasks). The correctness of the Bfair algorithm to meet the deadlines of all tasks’ instances is formally proved and its performance is evaluated through extensive simulations. The results show that, compared to that of Pfair algorithms, Bfair can significantly reduce the number of scheduling points (by up to 94%) and the overhead of Bfair at each scheduling point is comparable to that of the most efficient Pfair algorithm (i.e., PD²). Moreover, by aggregating the time allocation of tasks for the time interval between consecutive period boundaries, the resulting Bfair schedule can dramatically reduce the number of required context switches and task migrations (as much as 82% and 85%, respectively) when compared to those of Pfair schedules, which in turn reduces the run-time overhead of the system.     

A load index and a transfer policy for global scheduling tasks with deadlines

Two important components of a global scheduling algorithm are its transfer policy and its location policy. While the transfer policy determines whether a task should be transferred, the location policy determines where it should be transferred. Many global scheduling algorithms have been proposed to schedule tasks with deadline constraints. These algorithms try to transfer tasks only when task's deadlines cannot be met locally or local load is high (i.e. they take only corrective measures). However, a scheduling algorithm that takes preventive measures in addition to corrective measures can reduce potential deadline misses substantially. In this paper we present: (a) a load index which characterizes the system state and is more conducive to preventive and corrective measures; (b) a new transfer policy which takes preventive measures by doing anticipatory task transfers in addition to corrective measures. The proposed transfer policy adapts better to the workload by availing of the accurate system state made available by the proposed load index. An algorithm making use of the new transfer policy and the new load index is shown to reduce the number of deadline misses significantly when compared to algorithms taking only corrective measures.     

基于任务备份的云计算容错调度算法

云计算所提供的服务面向庞大的用户群,随着节点规模的扩大、任务执行时间的增长,云计算的故障率越来越高。为此,提出基于任务备份的云计算容错调度算法。将任务映射到含有该任务输入数据且负载最小的节点,根据云计算的安全等级将任务进行备份,并重新调度失败任务。仿真实验结果表明,该算法具有较好的容错性,任务调度成功率达到99%。     

Static scheduling of directed acyclic data flow graphs onto multiprocessors using particle swarm optimization

An efficient method based on particle swarm optimization (PSO) is developed to solve the Multiprocessor Task Scheduling Problem (MPTSP). To efficiently execute parallelized programs on a multiprocessor environment, a scheduling problem must be solved to determine the assignment of tasks to the processors, the execution order of the tasks, and the starting time of each task, such that some optimality criteria are met. The scheduling problem is known as an NP-complete problem even when the target processors are fully connected and no communication delay is considered among the tasks in the task graph. The complexity of the scheduling problem depends on the number of tasks (N), the number of processors (M), the task processing time and the precedence constraints. The Directed Acyclic Graph (DAG) was exploited to represent the tasks and their precedence constraints. The proposed algorithm was compared with the Genetic Algorithm (GA) and the Duplication Scheduling Heuristic (DSH). We also provide a systematic investigation on the effect of varying problem settings. The results show that the proposed algorithm could not outperform the DSH while it could outperform the GA in some cases.     

Priority-Driven Scheduling of Periodic Task Systems on Multiprocessors

The scheduling of systems of periodic tasks upon multiprocessor platforms is considered. Utilization-based conditions are derived for determining whether a periodic task system meets all deadlines when scheduled using the earliest deadline first scheduling algorithm (EDF) upon a given multiprocessor platform. A new priority-driven algorithm is proposed for scheduling periodic task systems upon multiprocessor platforms: this algorithm is shown to successfully schedule some task systems for which EDF may fail to meet all deadlines.     

A non-preemptive scheduling algorithm for soft real-time systems

Real-time systems are often designed using preemptive scheduling and worst-case execution time estimates to guarantee the execution of high priority tasks. There is, however, an interest in exploring non-preemptive scheduling models for real-time systems, particularly for soft real-time multimedia applications. In this paper, we propose a new algorithm that uses multiple scheduling strategies for efficient non-preemptive scheduling of tasks. Our goal is to improve the success ratio of the well-known Earliest Deadline First (EDF) approach when the load on the system is very high and to improve the overall performance in both underloaded and overloaded conditions. Our approach, known as group-EDF (gEDF) is based on dynamic grouping of tasks with deadlines that are very close to each other, and using Shortest Job First (SJF) technique to schedule tasks within the group. We will present results comparing gEDF with other real-time algorithms including, EDF, Best-effort, and Guarantee, by using randomly generated tasks with varying execution times, release times, deadlines and tolerance to missing deadlines, under varying workloads. We believe that grouping tasks dynamically with similar deadlines and utilizing a secondary criteria, such as minimizing the total execution time (or other metrics such as power or resource availability) for scheduling tasks within a group, can lead to new and more efficient real-time scheduling algorithms.     

<Emphasis FontCategory="SansSerif">LRE</Emphasis>-<Emphasis FontCategory="SansSerif">TL</Emphasis>: an optimal multiprocessor algorithm for sporadic task sets with unconstrained deadlines

This article presents a detailed discussion of LRE-TL (Local Remaining Execution-TL-plane), an algorithm that schedules hard real-time periodic and sporadic task sets with unconstrained deadlines on identical multiprocessors. The algorithm builds upon important concepts such as the TL-plane construct used in the development of the LLREF algorithm (Largest Local Remaining Execution First). This article identifies the fundamental TL-plane scheduling principles used in the construction of LLREF . These simple principles are examined, identifying methods of simplifying the algorithm and allowing it to handle a more general task model. For example, we identify the principle that total local utilization can never increase within any TL-plane as long as a minimal number of tasks are executing. This observation leads to a straightforward approach for scheduling task arrivals within a TL-plane. In this manner LRE-TL can schedule sporadic tasks and tasks with unconstrained deadlines. Like LLREF, the LRE-TL scheduling algorithm is optimal for task sets with implicit deadlines. In addition, LRE-TL can schedule task sets with unconstrained deadlines provided they satisfy the density test for multiprocessor systems. While LLREF has a O(n ²) runtime per TL-plane, LRE-TL’s runtime is O(nlog n) per TL-plane.     

Fault tolerance aware scheduling technique for cloud computing environment using dynamic clustering algorithm

In cloud computing, resources are dynamically provisioned and delivered to users in a transparent manner automatically on-demand. Task execution failure is no longer accidental but a common characteristic of cloud computing environment. In recent times, a number of intelligent scheduling techniques have been used to address task scheduling issues in cloud without much attention to fault tolerance. In this research article, we proposed a dynamic clustering league championship algorithm (DCLCA) scheduling technique for fault tolerance awareness to address cloud task execution which would reflect on the current available resources and reduce the untimely failure of autonomous tasks. Experimental results show that our proposed technique produces remarkable fault reduction in task failure as measured in terms of failure rate. It also shows that the DCLCA outperformed the MTCT, MAXMIN, ant colony optimization and genetic algorithm-based NSGA-II by producing lower makespan with improvement of 57.8, 53.6, 24.3 and 13.4 % in the first scenario and 60.0, 38.9, 31.5 and 31.2 % in the second scenario, respectively. Considering the experimental results, DCLCA provides better quality fault tolerance aware scheduling that will help to improve the overall performance of the cloud environment.

     

Cost-aware reliability task scheduling of automotive cyber-physical systems

New generation of automotives has become a typical Cyber-Physical System, including various sensors, complex communication networks and distributed computing devices. Manufacturers pay great attention to functional safety and cost control of automotive. High-end computing devices such as electronic control units (ECU) that can perform multiple tasks bring higher reliability and also lead to soaring costs. In this paper, we propose an efficient scheduling algorithm that satisfies cost and functional reliability constraints under multifunctional mixed-criticality task replication execution scenarios. Using the proposed fault-tolerant hardware cost-aware multifunctional safety assurance (FT_HCMSA) algorithm can reduce the deadline miss ratio (DMR) of high-criticality function significantly while meeting the autumotive functional safety and hardware cost constraints. FT_HCMSA uses a fine-grained ECU replacement strategy to optimize task allocation. Our experiments validate that FT_HCMSA can reduce the DMR of high-criticality level functions while meet the requirements of safety and cost.     

基于离线利用率分析的混合关键级任务调度优化研究

现主流的混合关键级调度算法在系统高关键级状态下时主要通过抛弃低关键级任务来保证高关键级任务的执行,进而保证系统的正确性。此方法常常导致低关键级任务无法执行但系统资源却过剩的问题发生,故基于该问题提出复合型SDU（schedule depend on utilization）调度算法。该方法根据任务集对系统资源需求情况的不同进行利用率区间的划分,通过对各个区间实际使用情况的分析,设计相应的子算法进行调度,并提出了SDU算法对应的可调度性判据。仿真实验结果表明,相较于混合关键级任务调度领域主流的EDF-VD（earliest deadline first-virtual deadline）算法,所提SDU算法可将系统对任务集的调度率提升30%,并在相同情况下将系统对低关键级任务的执行率提升165%,证明了该算法可以极大地提高系统资源使用率,并保证系统服务完整性。     

Holistic analysis of asynchronous real-time transactions with earliest deadline scheduling

In distributed real-time systems, an application is often modeled as a set of real-time transactions, where each transaction is a chain of precedence-constrained tasks. Each task is statically allocated to a processor, and tasks allocated on the same processor are handled by a single-processor scheduling algorithm. Precedence constraints among tasks of the same transaction are modeled by properly assigning scheduling parameters as offsets, jitters and intermediate deadlines.In this paper we address the problem of schedulability analysis of distributed real-time transactions under the earliest deadline first scheduling algorithm. We propose a novel methodology that reduces the pessimism introduced by previous methods by explicitly taking into account the offsets of the tasks. Moreover, we extend the analysis to account for blocking time due to shared resources. In particular, we propose two kinds of schedulability tests, CDO-TO and MDO-TO, and show, with an extensive set of simulations, that they provides improved schedulability conditions with respect to classical algorithms. Finally, we apply the methodology to an important class of systems: heterogeneous multiprocessor systems, with a general purpose processor and one or more coprocessors (DSPs).