优化能耗的可变电压禁忌任务调度算法

能耗是影响异构式并行和分布式系统性能的一个重要因素,动态电压缩放(DVS)技术通过将处理器降低到不同频率来达到有效地节约能耗的目标。通常DVS技术包含任务调度及空闲时间片分配两阶段。当前绝大部分研究均针对时间片分配阶段,而在此考虑的是任务分配与空闲时间片间的关系。为了降低异构分布式系统的能耗,提出了一个利用禁忌(Tabu)策略进行调度的DVS算法。此算法首先调度用有向无环图(DAG)表示的任务集到处理器上,再应用禁忌策略来改进它,通过禁止任务再调度到特定处理器,从而增加时间片,分配阶段可用的空闲时间片达到进一步减少能耗的目标。仿真结果表明,本算法能有效地减少计算机系统的能耗。     

基于异构分布式系统的实时容错调度算法

目前文献中研究的实时容错调度算法都是基于同构分布式系统，系统中的所有处理机完全相同。该文首先建立了一个基于异构分布式系统实时容错调度模型，异构分布式系统中的各个处理机均不相同。基于该异构分布式系统模型，该文引入了可靠性代价（reliability cost）概念，并提出两种静态实时容错调度算法（RTFTNO和RTFTRC）用于调度周期性实时容错任务。算法RTFTRC在调度任务时，尽量使系统的可靠性代价最小；而算法RTFTNO在调度实时任务时，没有考虑系统的可靠性代价。该文详细讨论了两种调度算法的性能。性能模拟实验分别比较了两个算法的可靠性代价，超时比率和可调度性；并研究了任务的计算时间与可靠性代价的关系以及调度长度阈值与最小处理机个数的关系。实验结果表明，算法RTFTRC的性能优于算法RTFTNO。     

高实时性异构多核处理器任务调度算法

在异构多核处理器条件下,Min-Min算法调度性能较好但在系统实时响应方面存在不足。最小空闲时间优先调度算法(LSF)、最早截止时间优先调度算法(EDF)和最大价值优先调度算法(HVF)虽然在系统任务调度响应实时性方面表现优异,但却不适用于异构多核处理器环境。为此,提出一种高实时性任务调度算法HRSA。在Min-Min调度算法的基础上融合LSF,EDF,HVF算法的调度策略,将任务能耗、任务完成价值和任务响应比相结合,在实现异构多核处理器任务动态调度的同时缩短系统对高实时性任务的响应时间。实验结果表明,相对于EDF算法和Min-Min算法,HRSA算法消耗单位能量所带来的价值较高,对高实时性任务处理的响应时间较短。     

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.     

面向动态异构多核处理器的公平调度算法

动态异构多核处理器的处理器核可动态调整的特征给操作系统调度算法带来了新的机遇和挑战.利用处理器核动态可调整的特征能更好地适应不同任务的运行需求,带来巨大的性能优化空间.然而也带来新的代价和更复杂的公平性的计算.为了解决面向动态异构多核处理器结构上的公平性调度问题,提出了一个基于集中式运行队列的调度模型,以降低调度算法在动态处理器核变化所带来的维护开销.并重新思考在动态异构处理器结构下公平性的定义,基于原有CFS调度算法提出新的HFS调度算法.HFS调度算法不仅能简单而有效地利用动态异构多核处理器的性能优势,而且能提供在动态异构多核处理器上的公平性调度.通过模拟SCMP,ACMP,DHCMP平台,证明了提出的HFS调度算法能够很好地发挥DHCMP结构的性能特征,比运行目前主流调度算法的SCMP和ACMP结构提升10.55%的用户级性能(ANTT),14.24%的系统吞吐率(WSU).     

实时异构系统的动态分批优化调度算法

提出了一种实时异构系统的动态分批优化调度算法,该算法采用的是在每次扩充当前局部调度时,按一定规则在待调度的任务集中选取一批任务,对该批任务中的每项任务在每个处理器上的运行综合各种因素构造目标函数,将问题转化为非平衡分配问题,一次性为这些任务都分配一个处理器或为每个处理器分配一项任务,使得这种分配具有最好的“合适性”,以增大未被调度任务的可行性.这种方法有效地提高了算法调度成功率.同时,为了评估该算法的性能,对其进行了大量的模拟,分析了一些任务参数的变化对算法调度成功率的影响,并与老算法的调度成功率进行了比较.模拟结果显示,新算法优于老算法.     

单处理器多外设实时系统全局能耗优化算法研究

在电池供电的实时嵌入式系统中,能耗是系统设计的一个重要研究问题.动态电压调度和动态电源管理是两种重要的节能技术.前者是动态改变处理器电压/频率,降低处理器能耗;而后者是动态调整片外设备的工作模式,减少片外设备能耗.目前只有少量研究把这两种技术综合在一起.本文研究支持这两种技术的嵌入式全系统实时任务节能调度问题.针对连续和离散处理器频率模型,论文分别提出高效的算法,通过计算系统运行的能耗最小处理器最优频率和设备最优空闲时间,来实现全系统综合节能的目的.实验模拟表明本文算法大大优于其他算法.     

实时异构系统的动态调度算法研究

实时多处理器系统是解决复杂时应用的有效手段,目前对实时多处理器调度算法的研究却大多集中在同构系统上,对实时异构系统的调度则研究得比较少,提出了一种新的实时异构系统的动态调度算法,该算法采用了集中式的调度方案,同时,引入了一个新的任务分配策略,从而通过提高任务可行性而提高了算的调度成功率,此外,为了评估该算法的性能,还进行了大量的模拟研究,由于近视算法经简单修改便可以应用到实时异构系统的动态调度中,因此,在模拟研究中,以近视算法作为基准,将其应用于实时异构系统动态调度时的性能与新算法进行了比较,模拟结果显示,在多种任务参数的取值下,新算法的调度成功率均高于近视算法。     

一种实时异构系统的集成动态调度算法

提出了一种实时异构系统的集成动态调度算法.该算法通过一个新的任务分配策略以及软实时任务的服务质量QoS(quality of service)降级策略,不仅以统一方式完成了对实时异构系统中硬、软实时任务的集成动态调度,而且提高了算法的调度成功率.同时,还进行了大量的模拟研究.这些模拟以传统的近视算法为基准,将其应用在实时异构系统集成动态调度时的调度成功率与新算法进行比较,模拟结果表明,在多种任务参数取值下,新算法的调度成功率均高于传统的近视算法.     

基于动态任务调度的STDS算法设计研究

任务调度是计算机多核处理器系统获得高性能的关键,而现有的多核任务调度算法研究,大多侧重于静态调度下的算法优化和负载均衡,对动态调度及动态负载均衡研究较少。针对动态调度,并结合异构多核的特点,提出一种基于核负载均衡的动态任务调度算法STDS。算法通过合理设定调度粒度,降低调度频率,从而减少调度消耗时间;根据异构多核处理器各核处理性能的差异,设置内核负载上下限值,控制内核负载保持在同一水平,以达到负载均衡效果。算法依据等待时间长短、任务间通信大小和内核负载轻重因素对任务进行实时调度,并可通过实时因子、负载因子等参数设置3种因素的影响比重,以满足系统的不同需求。仿真实验显示,在内核数目较多的系统中,STDS算法更加高效,在保证任务处理速度的同时有较好负载均衡。     

Optimal virtual cluster-based multiprocessor scheduling

Scheduling of constrained deadline sporadic task systems on multiprocessor platforms is an area which has received much attention in the recent past. It is widely believed that finding an optimal scheduler is hard, and therefore most studies have focused on developing algorithms with good processor utilization bounds. These algorithms can be broadly classified into two categories: partitioned scheduling in which tasks are statically assigned to individual processors, and global scheduling in which each task is allowed to execute on any processor in the platform. In this paper we consider a third, more general, approach called cluster-based scheduling. In this approach each task is statically assigned to a processor cluster, tasks in each cluster are globally scheduled among themselves, and clusters in turn are scheduled on the multiprocessor platform. We develop techniques to support such cluster-based scheduling algorithms, and also consider properties that minimize total processor utilization of individual clusters. In the last part of this paper, we develop new virtual cluster-based scheduling algorithms. For implicit deadline sporadic task systems, we develop an optimal scheduling algorithm that is neither Pfair nor ERfair. We also show that the processor utilization bound of us-edf{m/(2m−1)} can be improved by using virtual clustering. Since neither partitioned nor global strategies dominate over the other, cluster-based scheduling is a natural direction for research towards achieving improved processor utilization bounds.

A design fix to supervisory control for fault-tolerant scheduling of real-time multiprocessor systems with aperiodic tasks

In the article ‘Supervisory control for fault-tolerant scheduling of real-time multiprocessor systems with aperiodic tasks’, Park and Cho presented a systematic way of computing a largest fault-tolerant and schedulable language that provides information on whether the scheduler (i.e., supervisor) should accept or reject a newly arrived aperiodic task. The computation of such a language is mainly dependent on the task execution model presented in their paper. However, the task execution model is unable to capture the situation when the fault of a processor occurs even before the task has arrived. Consequently, a task execution model that does not capture this fact may possibly be assigned for execution on a faulty processor. This problem has been illustrated with an appropriate example. Then, the task execution model of Park and Cho has been modified to strengthen the requirement that none of the tasks are assigned for execution on a faulty processor.     

Group-Based Pfair Scheduling

We consider the problem of supertasking in Pfair-scheduled multiprocessor systems. In this approach, a set of tasks, called component tasks, is assigned to a server task, called a supertask, which is then scheduled as an ordinary Pfair task. Whenever a supertask is scheduled, its processor time is allocated to its component tasks according to an internal scheduling algorithm. Hence, supertasking is an example of hierarchal (or group-based) scheduling. In this paper, we present a generalized framework for “reweighting” supertasks. The goal of reweighting is to assign a fraction of a processor to a given supertask so that all timing requirements of its component tasks are met. We consider the use of both fully preemptive and quantum-based scheduling within a supertask. Work supported by NSF grants CCR 9732916, CCR 9972211, CCR 9988327, ITR 0082866, CCR 0204312, and CCR 0309825. Preliminary versions of some content appeared previously in (Holman and Anderson, 2001, 2003).     

嵌入式并行系统中基于任务优化的调度算法

在嵌入式并行计算系统中,任务调度是决定系统性能的关键。多任务调度中,启发式调度法是一种设计简单且性能良好的调度方法。目前的调度算法大多是基于任务复制的,没有充分考虑前驱任务与其后继任务间的相关性。该文提出了一种基于相关任务优化(DTO)的调度算法,通过分析已用处理机的负载和空闲时间,尽量减少系统的调度长度和处理机数目。算法分析结果表明,DTO算法在性能上优于其他算法,对嵌入式并行计算系统中的多任务调度是一个较好的选择。     

Utilization Bounds for N-Processor Rate Monotone Scheduling with Static Processor Assignment

We consider the schedulability of a set of independent periodic tasks under fixed priority preemptive scheduling on homogeneous multiprocessor systems. Assuming there is no task migration between processors and each processor schedules tasks preemptively according to fixed priorities assigned by the Rate Monotonic policy, the scheduling problem reduces to assigning the set of tasks to disjoint processors in such a way that the Monotonic policy, the scheduling problem reduces to assigning the set of tasks to disjoint processors in such a way that the schedulability of the tasks on each processor can be guaranteed. In this paper we show that the worst case achievable utilization for such systems is between n(2^1/2-1) and (n+1)/(1+2^1/(n+1)), where n stands for the number of processors. The lower bound represents 41 percent of the total system capacity and the upper bound represents 50 to 66 percent depending on n. Practicality of the lower bound is demonstrated by proving it can be achieved using a First Fit scheduling algorithm.     

一种基于两级DAG模型的MapReduce工作流异构调度算法

MapReduce编程模型被广泛应用于大数据处理平台,而一个有效的任务调度算法对模型的运行效率至关重要。将MapReduce工作流的Map和Reduce阶段分别拆解为若干个有先后序限定关系的作业,每个作业再拆解为多个任务。之后基于计算集群的可用资源和任务异构性,构建面向作业和任务的2级有向无环图(DAG)模型,同时提出基于2级优先级排序的异构调度算法2-MRHS。算法的第1阶段进行优先级排序,即对作业和任务分别进行优先权值计算,再汇总得到任务的调度队列;第2阶段进行任务分配,即基于最快完成时间将每个任务所包含的数据块子任务分配给最适合的计算结点。采用大批量随机生成的DAG模型进行实验,结果表明与其他相关算法相比,本文算法有更短的调度长度(makespan)且更加稳定。     

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.     

基于HEFT和CPOP的相关任务表调度算法

DAG任务调度是当前研究的热点,DAG任务模型中任务的调度顺序一方面会影响用户服务满意质量,另一方面也会影响云服务资源的利用率,高效的任务调度算法能够使多核处理器的资源分配和并行计算能力更强.表调度算法HEFT算法以及CPOP算法在相关任务调度中存在效率较低等问题.本文基于HEFT算法和CPOP算法,提出了一种相关任务调度模型和相关任务调度算法IHEFT算法,对任务排序和任务调度两个方面进行改进.任务排序阶段,以任务的方差以及平均通信代价作为排序的依据;任务调度阶段,对满足任务复制条件的结点进行任务复制.实验证明,IHEFT算法在任务调度跨度、任务调度平均等待时间以及平均Slack值方面均优于HEFT算法和CPOP算法.     

提高软非周期任务响应性能的调度算法

实时环境中常常既包含硬周期任务,又包含软非周期任务,引入一种改进软非周期实时任务响应时间的算法.已有的解决混合任务调度问题的方法都是基于速率单调（Rate Monotonic）策略的,其中从周期任务“挪用时间”的算法被证明优于其他所有算法.但是,速率单调算法限制了处理器的使用率,从而使周期任务的可“挪用”时间受到限制.最后期限驱动（Deadline Driven）策略DD可使潜在的处理器利用率达到100%.新算法正是在周期任务的调度中适当加入了DD策略,从而使非周期任务的响应时间得以缩短.仿真实验的结果表明,这种算法的性能优于已有的所有算法,而由它所带来的额外开销却不算很高.