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.     

一种云工作流任务调度能效优化算法

工作流任务执行时带来的高能耗不仅会增加云资源提供方的经济成本,而且会降低云系统的可靠性。为了满足截止时间的同时,降低工作流执行能耗,提出一种工作流能效调度算法CWEES。算法将能效优化调度划分为三个阶段：初始任务映射、处理器资源合并和任务松驰。初始任务映射旨在通过任务自底向上分级排序得到任务调度初始序列,处理器资源合并旨在通过重用松驰时间合并相对低效率的处理器,降低资源使用数量,任务松驰旨在为每个任务重新选择带有合适电压/频率等级的最优目标资源,在不违背任务顺序和截止时间约束前提下降低工作流执行总能耗。通过随机工作任务模型对算法的性能进行了仿真实验分析。结果表明,CWEES算法不仅资源利用率更高,而且可以在满足截止时间约束下降低工作流执行能耗,实现执行效率与能耗的均衡。     

基于任务复制的网格任务调度算法

网格中资源之间存在着通信延迟,通过任务复制的冗余,可以减少任务之间的通信开销,缩短整个计算程序的计算时间。目前网格中的任务调度算法基本上是没有考虑任务复制的;而基于任务复制调度算法往往会产生过多的复制任务,增大系统开销,甚至有可能延迟计算时间。由于基于任务复制的任务调度是一个NP问题,因此本文提出了一种基于任务复制的网格资源调度算法,以减少调度长度为主要目标、减少任务复制量和资源占用量为次要目标。该算法在调度长度和任务复制数量以及占用资源数量方面都等于或优于其它算法。     

提高卫星服务寿命的任务规划方法研究

基于组网卫星协同工作规划可提高复杂任务处理的基础上,本文探索降低规划风险,提高卫星服务寿命的卫星任务规划方法.从提高卫星服务寿命角度,建立用于描述资源使用期望的目标规划模型;建立卫星任务规划模型,在任务方案与资源使用中考虑了规划风险要素,并将资源使用目标模型引入到任务规划模型中,以期在规划中同时优化卫星服务寿命要素;基于邻域贪心扩展改善遗传禁忌求解算法,并运用分布并行技术提高工程实用性;利用STK提供基础仿真数据,对模型与算法进行了验证.     

物联网环境下具有顺序约束关系的静态任务表调度算法

针对物联网异构调度环境下并行计算的静态任务调度问题,提出了一种基于最早完成时间策略改变调度顺序的表调度算法HDPTS。该算法针对现有表调度算法在调度前不能准确地确定调度顺序的问题,在IHEFT算法的基础上添加了一个动态优先级调度策略,当节点的前驱任务都已经完成调度任务时,就改变该节点的调度优先级。任务优先级的计算在所有前驱任务到达这个任务的最晚完成时间与所有资源上最大可以使用时间之间取最大值的基础上,同时考虑到分配到各个资源上的任务对后继任务的影响和资源上的负载情况,以及上行权重的计算值和对出口任务的影响,使得优先级计算更加合理,能够根据任务分配动态合理改变任务调度顺序。通过随机生成一个算例进行测试,结果表明HDPTS比IHEFT、HEFT在调度长度方面减少14.29%;对大量随机产生的特定结构的有向无环图(DAG)进行测试,测试结果显示HDPTS算法比IHEFT、HEFT和LDCP算法更有效。     

Dynamic scheduling of a batch of parallel task jobs on heterogeneous clusters

This paper addresses the problem of minimizing the scheduling length (make-span) of a batch of jobs with different arrival times. A job is described by a direct acyclic graph (DAG) of parallel tasks. The paper proposes a dynamic scheduling method that adapts the schedule when new jobs are submitted and that may change the processors assigned to a job during its execution. The scheduling method is divided into a scheduling strategy and a scheduling algorithm. We also propose an adaptation of the Heterogeneous Earliest-Finish-Time (HEFT) algorithm, called here P-HEFT, to handle parallel tasks in heterogeneous clusters with good efficiency without compromising the makespan. The results of a comparison of this algorithm with another DAG scheduler using a simulation of several machine configurations and job types shows that P-HEFT gives a shorter makespan for a single DAG but scores worse for multiple DAGs. Finally, the results of the dynamic scheduling of a batch of jobs using the proposed scheduler method showed significant improvements for more heavily loaded machines when compared to the alternative resource reservation approach.     

耦合分布式仿真中任务调度的研究与实现

在耦合分布式仿真系统中,为解决任务对资源合理利用的问题,采用一种First—Available调度策略,即在对资源和任务有效管理的基础上,根据工况计算任务的资源请求,在资源表中选取第一个符合要求的计算节点,指定此节点运行某项工况任务。测试表明,该调度策略能够快速有效匹配任务的资源请求,有非常强的实用价值和实效性,能够有效合理地利用资源,满足了大规模多工况多任务耦合仿真计算的要求。     

计算机控制中的模糊调度设计

研究了具有模糊截止期的多控制任务的实时调度问题,提出了奉献度的概念和最大奉献优先（LDF）的调度策略．为了减小因任务间频繁切换造成的系统开销。提出了基于抢占阈值的最大奉献优先（TLDF）调度策略．最后,通过仿真比较了LDF和TLDF两种调度策略,实现了具有模糊截止期的控制任务调度,在减少并均衡控制性能损失的同时提高系统计算资源的使用率．     

Benchmark-problem instances for static scheduling of task graphs with communication delays on homogeneous multiprocessor systems

Scheduling program tasks on processors is at the core of the efficient use of multiprocessor systems. Most task-scheduling problems are known to be NP-Hard and, thus, heuristics are the method of choice in all but the simplest cases. The utilization of acknowledged sets of benchmark-problem instances is essential for the correct comparison and analysis of heuristics. Yet, such sets are not available for several important classes of scheduling problems, including multiprocessor scheduling problem with communication delays (MSPCD) where one is interested in scheduling dependent tasks onto homogeneous multiprocessor systems, with processors connected in an arbitrary way, while explicitly accounting for the time required to transfer data between tasks allocated to different processors. We propose test-problem instances for the MSPCD that are representative in terms of number of processors, type of multiprocessor architecture, number of tasks to be scheduled, and task graph characteristics (task execution times, communication costs, and density of dependencies between tasks). Moreover, we define our task-graph generators in a way appropriate to ensure that the corresponding problem instances obey the theoretical principles recently proposed in the literature.     

一种无线网络控制系统的时空实时任务调度算法

提出了一种无线网络控制系统的时间空间混合优先级调度算法(STS)。该算法综合考虑了无线网络控制系统中机器人之间与任务区域的距离和任务有效截止期,将时间和距离两个因素通过多个步骤逐步量化,最终形成最优的优先级队列,移动机器人根据优先级队列逐个解决区域的任务需求。为了验证STS算法的性能,做了大量的仿真实验来证明时间空间混合优先级算法的优势。实验结果表明,在确保高优先级调度成功并提高系统稳定性的前提下,时间空间混合优先级算法在任务请求吞吐率、任务解决成功率和平均响应时间等方面与之前的算法相比都具有更好的调度表现。     

分布式网格系统的任务调度算法DE Scheduling

目前研究的动态任务调度算法都基于集中式或部分分布式网格系统,系统中心节点(组)进行资源管理。该文提出一种面向无资源管理的完全分布式网格系统动态任务调度算法DE Scheduling。该算法使用任务冗余调度算法屏蔽解决系统的动态性问题,通过动态调节冗余量减少无效计算和保证系统负载均衡。使用给定平均连接度的无标度网络演化模型构造具有1 000个节点的Internet网络模型仿真任务处理过程。仿真结果表明,任务数为10 000~100 000时该系统冗余调度次数均为2次,冗余计算量占总计算量的比例不超过0.35%,且随着任务数增加而递减。     

基于任务-资源分配图优化选取的网格依赖任务调度

任务调度是网格应用系统获得高性能的关键.网格计算中一个大型的应用程序往往被分解为具有依赖关系的多个任务.在资源个体差异较大、广域互连的网格环境下任务间的依赖关系对传统的调度策略提出了新的挑战.任务调度的主要工作是为任务分配资源以及确定任务的执行次序,将依赖任务的可能的资源分配方案表示为任务-资源分配图（T-RAG）,在该图的基础上提出了基于T-RAG优化选取的依赖任务调度模型,将依赖任务调度问题转化为图的优化选取问题,解析最优任务-资源分配图可以同时确定资源分配方案和任务的执行次序即为最优调度方案.最后,实现了基于该模型的任务调度算法,该算法与ILHA算法的对比分析表明,在资源差异较大及任务间存在大量数据传输的情况下所提出的算法更优.     

Static scheduling of multiple workflows with soft deadlines in non-dedicated heterogeneous environments

Typical patterns of using scientific workflows include their periodical executions using a fixed set of computational resources. Using the statistics from multiple runs, one can accurately estimate task execution and communication times to apply static scheduling algorithms. Several workflows with known estimates could be combined into a set to improve the resulting schedule. In this paper, we consider the mapping of multiple workflows to partially available heterogeneous resources. The problem is how to fill free time windows with tasks from different workflows, taking into account users’ requirements of the urgency of the results of calculations. To estimate quality of schedules for several workflows with various soft deadlines, we introduce the unified metric incorporating levels of meeting constraints and fairness of resource distribution.The main goal of the work was to develop a set of algorithms implementing different scheduling strategies for multiple workflows with soft deadlines in a non-dedicated environment, and to perform a comparative analysis of these strategies. We study how time restrictions (given by resource providers and users) influence the quality of schedules, and which scheme of grouping and ordering the tasks is the most effective for the batched scheduling of non-urgent workflows. Experiments with several types of synthetic and domain-specific sets of multiple workflows show that: (i) the use of information about time windows and deadlines leads to the significant increase of the quality of static schedules, (ii) the clustering-based scheduling scheme outperforms task-based and workflow-based schemes. This was confirmed by an evaluation of studied algorithms on a basis of the CLAVIRE workflow management platform.     

基于动态关键路径的仿真网格资源调度算法

从仿真系统工作流的关键路径分析，确定关键路径上的联邦成员节点，使网格资源调度方面优先获得保证。簦于仿真网格系统运行的复杂性和不确定性，引入了随机规划理论，提出了仿真网格的动态关键路径概念以及基于动态关键路径的资源调度算法，共同解决当资源有限以及任务相关情况下仿真网格的资源调度问题。具体实现，主要使用遗传算法以及MCP算法。仿真实验结果表明，基于动态关键路径的资源调度算法能够优化仿真网格资源的调度。     

基于 DPSO 的云工作流任务自适应调度策略

云工作流系统中的任务调度问题属于典型的NP难题,同时由于计算资源异构性、复杂性及用户需求的动态性可能导致系统过载。为了解决或避免此类问题的发生,本文提出一种带动态反馈机制的任务自适应分配方法,并结合离散粒子群优化算法（ Discrete Particle Swarm Optimization ,DPSO）,利用任务预测执行时间模型来优化任务分配方案。仿真实验表明该方法可保证系统负载平衡,当任务数大于150时能够使任务调度时间最短。     

Two Job Cyclic Scheduling With Incompatibility Constraints

The present paper deals with the problem of scheduling several repeated occurrences of two jobs over a finite or infinite time horizon in order to maximize the yielded profit. The constraints of the problem are the incompatibilities between some pairs of tasks which require a same resource.     

同构Hadoop集群环境下改进的延迟调度算法

在Hadoop框架下计算资源和数据资源可以在不同物理位置的特点产生本地化问题。延迟调度算法的产生旨在解决本地化问题, 此算法根据任务待处理数据的物理位置作为作业的计算节点, 调度任务至目标节点。但是可能出现同一作业中若干任务集中运行在某一计算节点, 导致作业达不到理想的并行效果。针对原有的延迟调度算法, 提出延迟一容量调度算法, 允许部分任务选择非本地化节点作为原延迟调度算法中任务的目标计算节点, 以提高作业的响应时间与增加作业的并行程度。最后通过实验对比分析, 改进后的算法在执行效率和并行效果明显优于原延迟调度算法。     

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。     

基于Kriging代理模型的动态云任务调度方法

任务调度在云计算环境中发挥着重要作用。提出一种基于Kriging代理模型的动态云任务调度方法。通过对云任务在不同资源组合下的性能表现进行Kriging代理模型建模并优化,从而得到对应于该云任务的最优资源分配方案;利用云平台的API,可动态对该云任务实施资源调度。基于OpenStack开源云平台,对两个工程计算应用进行了任务调度性能测试,结果表明该方法可有效动态调整云任务中的资源配给,按需按优对平台中的云任务进行资源调度。     

用神经网络求解机器人动态调度中的近似指派问题

本文利用HOPFIELD神经网络，对机器人动态调度中的近似指派问题提出了合理的神经网络表示方法，给出了网络的能量函数表示法及神经元状态方程，从而得出了机器人动态调度中近似指派问题的快速求解策略，满足了动态调度的实时性要求。本文从理论上论证了所提算法的收敛性。软件仿真结果表明，本文提出的近似指派问题网络求解方法是有效的，计算结果是满意的。