异构分布式系统中实时周期任务的容错调度算法

提出一个基于抢占性实时周期任务的可靠性调度模型,该模型与现有可靠性模型相比充分考虑了单处理机故障容错情况下的系统可靠性,因而更加接近现实和精确.在此基础上,提出一个基于异构分布式系统的实时容错调度算法IRDFTAHS,IRDFTAHS算法以提高系统的可靠性为目标来进行任务的分配,从而在不增加硬件代价的前提条件下通过调度增加了系统的可靠性.该算法同时支持主动和被动两种方式的副版本,使得容错调度算法具有更大的灵活性.最后,通过仿真实验对IRDFTAHS和现有的调度算法在几个方面进行比较.实验结果表明,IRDFTAHS算法的综合性能优于现有算法.     

异构分布系统的实时任务轮转式容错调度算法

建立了一个异构分布式系统实时调度模型,对异构分布式系统中的任务及不同处理机资源进行了形式化描述.结合基版本/副版本技术,给出了用于异构分布式系统的实时任务轮转式容错调度算法.实例分析表明,该算法有效提高了异构处理机环境下的资源利用率以及整体计算性能.     

异构分布式系统混合型实时容错调度算法

基/副版本技术是实现实时分布式系统容错的一个重要手段。提出了一种异构分布式混合型容错模型,该模型与传统的异构分布式实时调度模型相比同时考虑了周期和非周期调度任务。在此基础上给出3种容错调度算法:以可调度性为目的SSA算法、以可靠性为目的RSA算法、以负载均衡性为目的BSA算法。算法能够在异构系统中同时调度具有周期和非周期容错需求的实时任务,且能够保证在异构系统中某节点机失效情况下,实时任务仍然能在截止时间内完成。最后从可调度性、可靠性代价、负载均衡性、周期与非周期任务数及任务周期与粒度J个方面对算法进行了分析。模拟实验结果显示算法各有优缺点,所以在选择调度算法时应该根据异构系统的特点来选择。     

混合型实时容错调度算法的设计和性能分析

以往文献中研究的实时容错调度算法都只能调度单一的具有容错需求的任务.该文建立了一个混合型实时容错调度模型,提出一种静态实时容错调度算法.该算法能同时调度具有容错需求的实时任务和无容错需求的实时任务.该文还提出了一个求解最小处理机个数的算法,用于对静态实时容错调度算法的性能进行模拟分析.为了提高静态调度算法的调度性能,提出了一种动态调度算法.最后,通过模拟实验分析了静态和动态调度算法的性能.实验表明,调度算法的性能与实时任务的个数、任务的计算时间、周期和处理机个数等系统参数相关.     

实时异构系统中的积极复制容错调度算法

在设计实时异构系统中的容错调度算法时,既要考虑到实时性的约束,又要最大化系统的可靠性.此外,异构系统中的并行应用调度问题已经被证明了是NP完全问题.现有的容错调度算法大多采用复制技术来提升系统的可靠性,但是任务的多次执行会导致应用执行时间变长,系统实时性下降.为此,提出了一个基于积极复制技术的容错调度算法,该算法连续的复制任务集中对当前系统实时性影响最小的任务,然后将任务集中的所有任务调度至最早完成的处理器,用以在满足实时性约束的同时,提升系统的可靠性.实验表明,相比于同样着眼于实时异构系统的DB-FTSA算法,该算法在实时性约束严格的情况下,可靠性有较大提升.     

云环境下两阶段安全驱动的容错调度算法

为解决云环境下安全调度和可靠性问题,综合考虑云计算共享性、动态性等特点,以具有依赖关系的并行任务为基础,提出一种两阶段安全驱动的容错调度算法(TSDFT).建立安全模型计算任务调度风险率,根据风险率选择处理机,使用自适应备份策略对任务进行备份预处理;在上一阶段基础上,通过被动副本方式实现任务容错调度,每个处理机维护主/副本2个局部队列,支持一个以上处理机同时失效.仿真结果表明,该算法能有效降低异构系统中任务调度风险率,提高调度的安全性和可靠性.     

基于EDF的分布式系统实时容错调度算法

将分布式系统的任务分配算法与处理器局部调度算法相结合，提出一种主动备份的、基于EDF的分布式系统实时容错调度算法，其特点是主／副版本执行时间可以重叠。给出了该调度算法的任务集可调度的充分条件、任务集可调度所需最小处理器个数的计算方法。模拟结果比较了主动备份容错调度算法与被动备份容错调度算法，结果表明卞动备份算法效率更优。     

分布式实时系统的容错调度算法

提出了两种分布式实时容错调度算法：副版本后调度算法（ＢＫＣＬ）及无容错需求后调度算法（ＮＦＲＬ）,并研究了算法的时间复杂度,这两种容雕工算法能同时调度具有容错需求的实时任务和无容错需求的实时任务,ＢＫＣＬ和ＮＦＲＬ所产生的调度可保证：在分布式系统中一个节点机失效的情况下,具有容错需求的实时任务仍然可在截止时间内完成,在描述了两个实时容错调度算法之后,分别证明了这两个算法的容错调度正确性。接着,阐述     

NFRL:一种分布系统的实时容错调度算法

在硬实时系统的应用中,如果硬实时任务不能在规定的时限完成,将会产生人员伤亡, 失等严重后果,为了保证在系统出错的情况下,硬实时任务仍然在能戴止时限之前完成,必须研究实时容错技术。本文从实时容错调度算法的角度出发,提出一种基于分布式系统的实时容错调度算法,并研究了该算法的时间复杂度,同时给出一个实例说明该容错调度算法的调度过程。这种容错调算法称为“无容错需求后调度算法（ＮＦＲＬ）,该实时容错调度算法     

异构分布式系统中基于负载均衡的容错调度算法

提出了基于主/从版本的具有容错功能的进程调度算法HDALF和HDLDF,且分别给出两种算法的时间复杂度并对算法的负载均衡性和节点资源利用率作了讨论.与以往容错调度算法不同的是,此算法是在被动进程复制模式下、适合于异构分布式系统的容错调度算法.而以往的研究都是建立在主从版本进程有相等的负载或执行时间相同的模型基础上,或者仅适合于同构分布式系统.实验结果表明,HDALF算法和HDLDF算法的性能比基于同构分布式模型下的两阶段算法更加优越.并且得出了这样的结果：当系统发生故障前后的负载均衡性权值相等时,在负载均衡和处理机资源利用率方面,HDLDF算法都要优于HDALF算法.     

Reliability-aware scheduling strategy for heterogeneous distributed computing systems

Heterogeneous computing systems are promising computing platforms, since single parallel architecture based systems may not be sufficient to exploit the available parallelism with the running applications. In some cases, heterogeneous distributed computing (HDC) systems can achieve higher performance with lower cost than single-machine supersystems. However, in HDC systems, processors and networks are not failure free and any kind of failure may be critical to the running applications. One way of dealing with such failures is to employ a reliable scheduling algorithm. Unfortunately, most existing scheduling algorithms for precedence constrained tasks in HDC systems do not adequately consider reliability requirements of inter-dependent tasks. In this paper, we design a reliability-driven scheduling architecture that can effectively measure system reliability, based on an optimal reliability communication path search algorithm, and then we introduce reliability priority rank (RRank) to estimate the task’s priority by considering reliability overheads. Furthermore, based on directed acyclic graph (DAG) we propose a reliability-aware scheduling algorithm for precedence constrained tasks, which can achieve high quality of reliability for applications. The comparison studies, based on both randomly generated graphs and the graphs of some real applications, show that our scheduling algorithm outperforms the existing scheduling algorithms in terms of makespan, scheduling length ratio, and reliability. At the same time, the improvement gained by our algorithm increases as the data communication among tasks increases.     

A high performance algorithm for static task scheduling in heterogeneous distributed computing systems

Effective task scheduling is essential for obtaining high performance in heterogeneous distributed computing systems (HeDCSs). However, finding an effective task schedule in HeDCSs requires the consideration of both the heterogeneity of processors and high interprocessor communication overhead, which results from non-trivial data movement between tasks scheduled on different processors. In this paper, we present a new high-performance scheduling algorithm, called the longest dynamic critical path (LDCP) algorithm, for HeDCSs with a bounded number of processors. The LDCP algorithm is a list-based scheduling algorithm that uses a new attribute to efficiently select tasks for scheduling in HeDCSs. The efficient selection of tasks enables the LDCP algorithm to generate high-quality task schedules in a heterogeneous computing environment. The performance of the LDCP algorithm is compared to two of the best existing scheduling algorithms for HeDCSs: the HEFT and DLS algorithms. The comparison study shows that the LDCP algorithm outperforms the HEFT and DLS algorithms in terms of schedule length and speedup. Moreover, the improvement in performance obtained by the LDCP algorithm over the HEFT and DLS algorithms increases as the inter-task communication cost increases. Therefore, the LDCP algorithm provides a practical solution for scheduling parallel applications with high communication costs in HeDCSs.     

Iterative list scheduling for heterogeneous computing

Optimal scheduling of parallel applications on distributed computing systems represented by directed acyclic graph (DAG) is NP-complete in the general case. List scheduling is a very popular heuristic method for DAG-based scheduling. However, it is more suited to homogenous distributed computing systems. This paper presents an iterative list scheduling algorithm to deal with scheduling on heterogeneous computing systems. The main idea in this iterative scheduling algorithm is to improve the quality of the schedule in an iterative manner using results from previous iterations. The algorithm first uses the heterogeneous earliest-finish-time (HEFT) algorithm to find an initial schedule and iteratively improves it. Hence the algorithm can potentially produce shorter schedule length. The simulation results show that in the majority of the cases, there is significant improvement to the initial schedule. The algorithm is also found to perform best when the tasks to processors ratio is large.     

Efficient task scheduling for budget constrained parallel applications on heterogeneous cloud computing systems

As the cost-driven public cloud services emerge, budget constraint is one of the primary design issues in large-scale scientific applications executed on heterogeneous cloud computing systems. Minimizing the schedule length while satisfying the budget constraint of an application is one of the most important quality of service requirements for cloud providers. A directed acyclic graph (DAG) can be used to describe an application consisted of multiple tasks with precedence constrains. Previous DAG scheduling methods tried to presuppose the minimum cost assignment for each task to minimize the schedule length of budget constrained applications on heterogeneous cloud computing systems. However, our analysis revealed that the preassignment of tasks with the minimum cost does not necessarily lead to the minimization of the schedule length. In this study, we propose an efficient algorithm of minimizing the schedule length using the budget level (MSLBL) to select processors for satisfying the budget constraint and minimizing the schedule length of an application. Such problem is decomposed into two sub-problems, namely, satisfying the budget constraint and minimizing the schedule length. The first sub-problem is solved by transferring the budget constraint of the application to that of each task, and the second sub-problem is solved by heuristically scheduling each task with low-time complexity. Experimental results on several real parallel applications validate that the proposed MSLBL algorithm can obtain shorter schedule lengths while satisfying the budget constraint of an application than existing methods in various situations.     

Data‐aware task scheduling on heterogeneous hybrid memory multiprocessor systems

In this paper, we propose a method about task scheduling and data assignment on heterogeneous hybrid memory multiprocessor systems for real‐time applications. In a heterogeneous hybrid memory multiprocessor system, an important problem is how to schedule real‐time application tasks to processors and assign data to hybrid memories. The hybrid memory consists of dynamic random access memory and solid state drives when considering the performance of solid state drives into the scheduling policy. To solve this problem, we propose two heuristic algorithms called improvement greedy algorithm and the data assignment according to the task scheduling algorithm, which generate a near‐optimal solution for real‐time applications in polynomial time. We evaluate the performance of our algorithms by comparing them with a greedy algorithm, which is commonly used to solve heterogeneous task scheduling problem. Based on our extensive simulation study, we observe that our algorithms exhibit excellent performance and demonstrate that considering data allocation in task scheduling is significant for saving energy. We conduct experiments on two heterogeneous multiprocessor systems. Copyright © 2016 John Wiley & Sons, Ltd.     

A hybrid heuristic–genetic algorithm for task scheduling in heterogeneous processor networks

Efficient task scheduling on heterogeneous distributed computing systems (HeDCSs) requires the consideration of the heterogeneity of processors and the inter-processor communication. This paper presents a two-phase algorithm, called H2GS, for task scheduling on HeDCSs. The first phase implements a heuristic list-based algorithm, called LDCP, to generate a high quality schedule. In the second phase, the LDCP-generated schedule is injected into the initial population of a customized genetic algorithm, called GAS, which proceeds to evolve shorter schedules. GAS employs a simple genome composed of a two-dimensional chromosome. A mapping procedure is developed which maps every possible genome to a valid schedule. Moreover, GAS uses customized operators that are designed for the scheduling problem to enable an efficient stochastic search. The performance of each phase of H2GS is compared to two leading scheduling algorithms, and H2GS outperforms both algorithms. The improvement in performance obtained by H2GS increases as the inter-task communication cost increases.     

一个有效的Join任务图的调度算法

已有的Join任务图的调度算法大多不是基于通信竞争的环境而开发,且未考虑节省处理机的问题,使算法的应用效果不佳.因此,针对Join任务图,提出一个通信竞争环境的调度算法,该算法因串行通信边而改善其调度效率,时间复杂度为O(vlogv),其中,v为图中任务的个数.实验结果表明,与其他算法相比,该算法的调度长度较短且使用的...     

基于动态关键任务的多处理器任务分配算法

多处理器调度问题是影响系统性能的关键问题,基于任务复制的调度算法是解决多处理器调度问题较为有效的方法.文中分析了几个典型的基于任务复制算法,提出了基于动态关键任务(DCT)的多处理器任务分配算法.DCT算法以克服贪心算法不足为要点,调度过程中动态计算任务时间参数,准确确定处理器的关键任务,以关键任务为核心优化调度,逐步改善调度结果,最终取得最优的调度结果.分析和实验证明,DCT算法优于现有其它同类算法.     

TSA—OT：一个调度Out—Tree任务科的算法

对于把一个任务群调度到多个处理器的问题,人们往往只注重找到一个调度路径最短的算法,却忽略了要节省处理器。收于Out-Tree任务图代表分治算法的一大类问题,因此,文中专门针对该任务图,给出了一个基于任务复制的算法TSA－OT。它首先分配关键路径上的任务结点,然后在不改变调度长度的情况下,把非关键路径上的结点尽可能分配到已用的处理器上。并且,该算法将Out-Tree任务图中的所有通信都化为零。TSA－OT算法与近几年所提出的TDS,CPFD,DCP算法之间的比较表明,TSA－OT算法不仅调度长度最短,而且采用了更少或相当个数的处理器。     

一个调度Fork-Join任务图的新算法

任务调度是影响工作站网络效率的关键因素之一.Fork-Join任务图可以代表很多并行结构,但其他已有调度Fork-Join任务图算法忽略了在非全互连工作站网络环境中通信之间不能并行执行的问题,有些效率高的算法又没有考虑节省处理器个数的问题.因此,专门针对该任务图,综合考虑调度长度、非并行通信和节省处理器个数问题,提出了一个基于任务复制的静态调度算法TSA_FJ.通过随机产生任务的执行时间和通信时间,生成了多个Fork-Join任务图,并且采用TSA_FJ算法和其他调度算法对生成的任务图进行调度.结果表明,