多处理器混合关键性系统中的划分调度策略

多核处理器正越发广泛地应用到现代嵌入式系统的设计与实现当中,其强大的计算能力为将多个不同关键性级别的功能子系统集成到统一的共享资源平台提供了支持.混合关键性系统的调度问题即便在单处理器平台中都极具挑战性,在多处理器平台则更为困难.将目前资源利用率最高的单处理器混合关键性调度算法EY-VD扩展到多处理器平台中.首先,结合传统的划分调度策略提出了适用于多处理器混合关键性系统的MC-PEDF(mixedcriticality partitioned earliest deadline first)划分调度算法.尽管比之前的算法有更好的可调度性能,但传统的划分策略不能有效地平衡不同关键性级别下的负载,故其不完全适用于混合关键性系统.为了克服传统策略的不足,提出了划分调度策略OCOP(one criticality one partition).OCOP允许系统在关键性模式切换时对实时任务集进行重新划分,进而更好地平衡各个处理器在不同关键性模式中的资源利用率.基于OCOP,提出了第2种划分调度算法MC-MP-EDF(mixed-criticality multi-partitioned EDF).基于随机生成任务集的仿真实验结果表明,与MC-PEDF和已有的算法相比,MC-MP-EDF能够显著地提高系统的可调度性,尤其是在处理器数量较多的系统中.     

处理顺序约束的信息物理融合系统静态任务表调度算法

针对异构环境并行计算的静态任务调度问题,以最小化有向无环图 (Directed acyclic graph, DAG)的执行跨度为目标,改变HEFT (Heterogeneous earliest finish time)算法中任务上行权重的计算方法, 获得更加合理的任务顺序排列,提出了一种最早完成时间优先的表调度算法IHEFT (Improvement heterogeneous earliest finish time).该算法在计算任务的上行权重时, 分别计算该任务分配给不同资源的上行权重,取其最小值,比使用所有资源对该任务的平均处理时间进行计算的HEFT算法更为准确. 确定任务的处理顺序后采用最早完成时间越小越优先的策略将任务分配给最优资源,并使得任务的开始执行时间和结束时间满足DAG中有向边的通讯时间约束.通过使用部分文献中的算例数据以及随机生成满足一定结构要求的DAG进行算法测试,将IHEFT与HEFT, CPOP (Critical-path-on-a-processor)和LDCP (Longest dynamic critical path)进行了比较,结果显示IHEFT算法更有效,而且时间复杂度较低.     

Fault-tolerant and real-time scheduling for mixed-criticality systems

The design and analysis of real-time scheduling algorithms for safety-critical systems is a challenging problem due to the temporal dependencies among different design constraints. This paper considers scheduling sporadic tasks with three interrelated design constraints: (i) meeting the hard deadlines of application tasks, (ii) providing fault tolerance by executing backups, and (iii) respecting the criticality of each task to facilitate system’s certification. First, a new approach to model mixed-criticality systems from the perspective of fault tolerance is proposed. Second, a uniprocessor fixed-priority scheduling algorithm, called fault-tolerant mixed-criticality (FTMC) scheduling, is designed for the proposed model. The FTMC algorithm executes backups to recover from task errors caused by hardware or software faults. Third, a sufficient schedulability test is derived, when satisfied for a (mixed-criticality) task set, guarantees that all deadlines are met even if backups are executed to recover from errors. Finally, evaluations illustrate the effectiveness of the proposed test.     

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.     

一种基于动态需求边界的混合关键级作业调度算法

把具有不同重要性的功能集成到一个共享平台上的混合关键级系统,是当前嵌入式系统发展的主要趋势之一.已有的混合关键级调度理论为了保证高关键级作业的完成,大多不支持关键级向下切换,在系统进入高关键级后直接放弃低关键级作业的执行,这对系统中作业集的整体完成率有负面影响.为了应对这一问题,把需求边界分析理论扩展到混合关键级作业系统中,提出了作业的动态需求边界函数,以矢量的形式记录系统在运行时需求边界函数的动态变化,并相应地提出了作业的混合关键级松弛时间与系统关键级松弛时间的概念.在此基础上,提出了一种基于动态需求边界的混合关键级作业调度算法CSDDB （criticality switch based on dynamical demand boundary）.该算法选择具有最小松弛时间的关键级作为执行关键级,在保证高关键级作业可调度的情况下,充分利用系统资源,尽可能地满足低关键级作业的执行.应用随机生成的任务集进行仿真实验,结果表明,与已有算法相比,CSDDB在系统关键级的保证与作业集整体完成率方面比现有算法有10%以上的提升.     

Granularity-based workflow scheduling algorithm for cloud computing

The workflow scheduling problem has drawn a lot of attention in the research community. This paper presents a workflow scheduling algorithm, called granularity score scheduling (GSS), which is based on the granularity of the tasks in a given workflow. The main objectives of GSS are to minimize the makespan and maximize the average virtual machine utilization. The algorithm consists of three phases, namely B-level calculation, score adjustment and task ranking and scheduling. We simulate the proposed algorithm using various benchmark scientific workflow applications, i.e., Cybershake, Epigenomic, Inspiral and Montage. The simulation results are compared with two well-known existing workflow scheduling algorithms, namely heterogeneous earliest finish time and performance effective task scheduling, which are also applied in cloud computing environment. Based on the simulation results, the proposed algorithm remarkably demonstrates its performance in terms of makespan and average virtual machine utilization.     

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.     

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.     

一种双匹配动态调度算法

提出了适于异构环境独立任务调度的双匹配动态调度算法（BM算法）．BM算法将任务与处理机实现双匹配，使大部分任务在执行时间最短而且完成时间最早的处理机上执行．对于无法实现双匹配的任务，采用最早完成时间最小者优先的策略进行调度．BM算法可以同时满足负载均衡和高吞吐率两个目标．BM算法与通常用作评测基准的Min-min算法的比较结果表明，BM算法的运行时间远少于Min-min算法，其调度跨度比Min-min算法减少约9％．     

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.     

异构分布式控制系统中实时任务的调度算法

分布式控制系统是一种应用极为广泛的异构分布式实时系统，系统中同时存在有多种实时任务，如何将这些任务分配到各个处理器上并保证它们的时限是系统关键技术之一．在结合启发式任务分配算法和单处理器任务调度算法的基础上，提出了一种分布式控制系统的调度算法．该算法考虑了各个处理器的负载均衡，同时又能满足所有任务的时限．仿真结果表明了算法的有效性．     

Cluster-based multicore real-time mixed-criticality scheduling

Cluster-based scheduling is recently gaining importance to be applied to mixed-criticality real-time systems on multicore processors platform. In this approach, the cores are grouped into clusters, and tasks that are partitioned among different clusters are scheduled by global scheduler in each cluster. This research work introduces a new cluster-based task allocation scheme for the mixed-criticality real-time task sets on multicore processors. For task allocation, smaller clusters sizes (sub-clusters) are used for mixed-criticality tasks in low criticality mode, while relatively larger cluster sizes are used for high criticality tasks in high criticality mode. In this research paper, the mixed-criticality task set is allocated to clusters using worst-fit heuristic. The tasks from each cluster are also allocated to its sub-clusters, using the same worst-fit heuristic. A fixed-priority response time analysis approach based on Audsley’s approach is used for the schedulability analysis of tasks in each cluster and sub-cluster. If the high criticality job is not completed after its worst case execution time in low mode, then the system is switched to high criticality mode. After mode switch, all the low criticalities tasks are discarded and only high criticality tasks are further executed in high criticality mode. Simulation results indicate that the percentage of schedulable task sets significantly increases under cluster scheduling as compared to partitioned and global mixed-criticality scheduling schemes.     

具有优先级特征的多媒体流的资源管理

本文研究了具有优先级特征的分布式多媒体流的资源管理，提出了一种基于节优先级的资源管理的设计方法，包括资源管理机制，资源管理策略，服务质量（ＱｏＳ）协商调整算法和高优先级节枪占算法，它能够保证稳定的具估优先级特征的多媒体流，能够极大地调度并发的多媒体流，特别是在系统资源不足时，能够最大限度地调度高优先级的多媒体流，并能保证各多媒体流量了的ＱｏＳ。     

一种适于异构环境的任务调度算法

针对异构环境独立任务调度问题提出两个调度原则,并基于Min-min算法提出优先级最小最早完成时间算法(Priority min-min,PMM).该算法将任务在各处理机上执行时间的标准误差作为任务的优先级.选取最早完成时间较小的k个任务,优先调度其中优先级最高的一个.在实验基础上分析了参数$k$对PMM算法性能的影响. PMM算法克服了min-min算法单纯追求局部最优的局限性,更适合于异构环境.实验数据表明PMM算法能有效地降低调度跨度,其性能比min-min算法有明显提高.     

Reusable generic design patterns for mixed-criticality systems based on DREAMS

Multi-core mixed-criticality systems are complex solutions that provide benefits regarding lower power consumption, size, weight and cost and better performance and scalability, compared with single-core architectures. However, these systems where virtualization mechanisms such as hypervisors are used for integrating functionalities with different criticality levels into the same hardware platform and where on-chip and off-chip communication systems are implemented for communicating, imply certification challenges due to their complexity. Those challenges to certification are supported by the fact that today’s safety-related standard focus on single computing systems where spatial and temporal interferences are quite probable. Multi-core architectures enable sharing resources (e.g., cache memory, I/Os) between more than one processor at the same time, facilitating the appearance of interferences which may hinder the achievement of the spatial and temporal independences.This paper analyses the certification challenges in mixed-criticality systems and identifies some reusable generic solutions to overcome those challenges. The solutions presented in this paper are integrated into a safety wind turbine system that follows the design style introduced in European project DREAMS.     

LBBA: An efficient online benefit-aware multiprocessor scheduling for QoS via online choice of approximation algorithms

Maximizing the benefit gained by soft real-time jobs in many applications and embedded systems is highly needed to provide an acceptable QoS (Quality of Service). This paper considers a benefit model for on-line preemptive multiprocessor scheduling. The goal is to maximize the total benefit gained by the jobs that meet their deadlines. This method prioritizes the jobs using their benefit density functions and schedules them in a real-time basis. We propose an online choice of two approximation algorithms in order to partition the jobs among identical processors at the time of their arrival without using any statistics. Our analysis and experiments show that we are able to maximize the gained benefit and decrease the computational complexity (compared to existing algorithms) while minimizing makespan (response time, also referred to as cost), with fewer missed deadlines and more balanced usage of processors. Our solution is applicable to a wide variety of soft real-time applications and embedded systems such as, but not limited to multimedia applications, medical monitoring systems or those with higher utilization such as bursty hosting servers.¹     

异构实时多处理器系统的动态调度算法研究

实时多处理器系统的动态调度算法一直是实时系统中的重要研究课题.根据异构实时多处理器的特点,提出了一种新的异构实时动态调度算法P_IEFT.该算法采用了一个新的处理器分配策略——将任务分配到能最早完成任务的处理器上.该策略能够缩短调度长度,提高后继任务被接受的可能性,从而能够提高成功调度率.模拟结果表明,该调度算法的成功调度率高于近视算法和节约算法的成功调度率.     

An efficient list-based task scheduling algorithm for heterogeneous distributed computing environment

The task scheduling in heterogeneous distributed computing systems plays a crucial role in reducing the makespan and maximizing resource utilization. The diverse nature of the devices in heterogeneous distributed computing systems intensifies the complexity of scheduling the tasks. To overcome this problem, a new list-based static task scheduling algorithm namely Deadline-Aware-Longest-Path-of-all-Predecessors (DA-LPP) is being proposed in this article. In the prioritization phase of the DA-LPP algorithm, the path length of the current task from all its predecessors at each level is computed and among them, the longest path length value is assigned as the rank of the task. This strategy emphasizes the tasks in the critical path. This well-optimized prioritization phase leads to an observable minimization in the makespan of the applications. In the processor selection phase, the DA-LPP algorithm implements the improved insertion-based policy which effectively utilizes the unoccupied leftover free time slots of the processors which improve resource utilization, further least computation cost allocation approach is followed to minimize the overall computation cost of the processors and parental prioritization policy is incorporated to further reduce the scheduling length. To demonstrate the robustness of the proposed algorithm, a synthetic graph generator is used in this experiment to generate a huge variety of graphs. Apart from the synthetic graphs, real-world application graphs like Montage, LIGO, Cybershake, and Epigenomic are also considered to grade the performance of the DA-LPP algorithm. Experimental results of the DA-LPP algorithm show improvement in performance in terms of scheduling length ratio, makespan reduction rate , and resource reduction rate when compared with other algorithms like DQWS, DUCO, DCO and EPRD. The results reveal that for 1000 task set with deadline equals to two times of the critical path, the scheduling length ratio of the DA-LPP algorithm is better than DQWS by 35%, DUCO by 23%, DCO by 26 %, and EPRD by 17%.     

Energy aware fixed priority scheduling in mixed-criticality systems

Most of studies about energy management for MC systems are based on dynamic priority scheme. The disadvantages of dynamic priority scheme are high system overhead and poor predictability. Unlike previous studies, we focus on the problem of scheduling mixed-criticality (MC) periodic tasks with minimizing energy consumption in MC systems based on fixed priority scheme. Firstly, we explain a criticality rate monotonic scheduling (CRMS) and propose the sufficient schedulability condition of CRMS. Secondly, we compute the energy minimization uniform scaled speed and present an optimal static solution algorithm based on CRMS. The extra workload of the high criticality level (HI) task executes with the maximum processor speed in the high criticality mode (HI-mode). But this algorithm does not exploit the slack time generated from the HI task in the low criticality mode (LO-mode). For energy efficiency, we propose a dynamic fixed priority energy minimization algorithm which exploits the slack time generated from the HI task in LO-mode to save energy. In addition, it combines a dynamic voltage and frequency scaling technique and a dynamic power management technique to reduce energy consumption. Finally, the experiments are applied to evaluate the performance of the proposed algorithm and the experimental results show that the proposed algorithm can save up 23.89% energy compared with other existing algorithms.     

Task scheduling for heterogeneous systems using an incremental approach

Effective scheduling of the tasks of a distributed application is one of the key factors in achieving improved performance. It results in an adequate utilization of the underlying resources and also reduces the total execution time of the application. Generating an optimal schedule for a distributed application is not a trivial task as it exists in the class of NP-complete problems. In this paper, a novel strategy called incremental subgraph earliest finish time (INCSEFT) is proposed. It is aimed at scheduling tasks on heterogeneous systems. It incorporates the use of a subgraph that grows incrementally by adding critical paths. At each step, the scheduling strategy attempts to minimize the schedule length. Considering a large set of nodes at an instance makes this approach perform better than other scheduling strategies used for heterogeneous systems. The experiments performed with several graphs show that the INCSEFT strategy produces significant improvement over the well-known HEFT, LOOKAHEAD and CEFT strategies used for scheduling heterogeneous systems.