多处理器EPDFPfair算法的可调度性判定

针对多处理器实时调度中的最早伪时限优先(EPDF)Pfair算法,分析了EPDF算法在M个处理器平台上的可调度利用率约束,根据基于利用率的充分可调度性判定,提出了一种改进的可调度性判定方法。这种方法可以得到更多的可调度任务集,从而使得满足判定的强实时系统和使用tie-breaking规则困难的动态任务系统的调度有较小的开销。实验结果表明,改进的可调度性判定方法增加了判为可调度的任务集数量,具有较好的性能。     

Utilization Bounds for EDF Scheduling on Real-Time Multiprocessor Systems

The utilization bound for earliest deadline first (EDF) scheduling is extended from uniprocessors to homogeneous multiprocessor systems with partitioning strategies. First results are provided for a basic task model, which includes periodic and independent tasks with deadlines equal to periods. Since the multiprocessor utilization bounds depend on the allocation algorithm, different allocation algorithms have been considered, ranging from simple heuristics to optimal allocation algorithms. As multiprocessor utilization bounds for EDF scheduling depend strongly on task sizes, all these bounds have been obtained as a function of a parameter which takes task sizes into account. Theoretically, the utilization bounds for multiprocessor EDF scheduling can be considered a partial solution to the bin-packing problem, which is known to be NP-complete. The basic task model is extended to include resource sharing, release jitter, deadlines less than periods, aperiodic tasks, non-preemptive sections, context switches, and mode changes.     

混合粒子群算法的异构多核处理器间任务调度

针对异构多核处理器间的任务调度问题,为了更好地发挥异构多核处理器间的平台优势,提出一种基于将有关联的且不在同一处理器上的任务进行复制的思想,从而使每个异构多核的处理器能独立执行任务,来减少不同处理器之间的通信开销,并且通过混合粒子群算法(HPSO)来调度异构多核处理器中的任务,避免由于当任意一个异构多核处理器由于任务分配过多而导致计算机不能及时且准确地得出结果.最后实验证明,对比传统的启发式分配方案和常见的遗传算法(GA),基于任务复制思想分配方案和混合粒子群算法(HPSO)具有更好的求解能力,并且可以提供执行时间更少的调度分配方案,具有较好的应用价值.     

副版本不可抢占的全局容错调度算法

容错是硬实时系统的关键能力,容错调度算法可以在有错误发生的情况下满足任务的实时性需求.在主副版本机制的容错调度算法中,主版本出错后留给副版本运行的时间窗口小,副版本容易错失截止期.针对副版本需要快速响应的问题,提出副版本不可抢占的全局容错调度算法FTGS-NPB（fault-tolerant global scheduling with non-preemptive backups）,赋予副版本全局最高优先级,使副版本在主版本出错后可以立刻获得处理器资源,并且在运行过程中不会被其他任务抢占.这样,副版本可以在最短时间内响应.分别基于截止期分析和响应时间分析建立了FTGS-NPB的可调度性测试,并分析了两种可调度性测试分别适用于不同的优先级分配算法.仿真实验结果表明,FTGS-NPB可以有效地减少实现容错的代价.     

Utilization Bounds for Multiprocessor Rate-Monotonic Scheduling

In this paper, we extend Liu and Layland's utilization bound for fixed priority scheduling on uniprocessors to homogeneous multiprocessor systems under a partitioning strategy. Assuming that tasks are pre-emptively scheduled on each processor according to fixed priorities assigned by the Rate-Monotonic policy, and allocated to processors by the First Fit algorithm, we prove that the utilization bound is (n–1)(2^1/2–1)+(m–n+1)(2^1/(m–n+1)–1), where m and n are the number of tasks and processors, respectively. This bound is valid for arbitrary utilization factors. Moreover, if all the tasks have utilization factors under a value , the previous bound is raised and the new utilization bound considering is calculated. Finally, simulation provides the average-case behavior.     

Polynomial Algorithms for a Two-Class Multiprocessor Scheduling Problem in Mobile Telecommunications Systems

In Universal Mobile Telecommunications Systems radio access, information packets of different services and users are sent across the air interface organized into radio frames. A radio frame is divided into a fixed number of time slots, different packets can share the same time slot and exactly one slot must be assigned to each packet. The packets of different services have divisible sizes and the slot capacity is limited. Quality of Service requirements set time intervals within which each packet must be scheduled. In this work, we address the problem of scheduling packets related to two different services to the time slots of a radio frame. The problem is a variant of the High-Multiplicity Multiprocessor Scheduling Problem in which there are two classes of jobs with divisible sizes, and the jobs must satisfy assignment restrictions. Two polynomial—time scheduling algorithms, maximizing the number of scheduled packets, and the weighted throughput, are provided.     

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.     

副版本优先级可提升的全局容错调度算法

在主副版本机制的全局容错调度中,副版本运行窗口短,采用优先级继承策略的副版本响应时间长,容易错失截止期.针对副版本实时性差的问题,提出基于优先级提升策略的全局容错调度算法(fault tolerant global scheduling with backup priority promotion, FTGS-BPP),通过赋予副版本比主版本高的优先级,减少副版本在运行过程中受到的干扰,缩短了副版本的响应时间,改善了副版本的实时性,从而减少了实现容错所需的额外处理器资源.仿真结果表明,和采用优先级继承策略的全局容错调度算法相比,FTGS-BPP在调度相同的任务集时明显降低了处理器资源需求.     

网络并行环境下多处理机任务的调度

随着网络技术的不断发展，网络上可供共享的资源越来越丰富，集群技术的兴起更是扩展了并行计算的环境．这种环境下系统中很多任务依赖于多种资源(或多个处理机)，称这样的任务为多处理机任务．本文研究基于多处理机任务的调度模型Pm{fix}Cmax,当m≥3时，这类调度问题是强NP—难的，所以只能寻求有好的逼近性能的多项式时间近似算法．文中给出了当m＝4或5时线性时间的近似调度算法，优于目前已有的最好结果，最后我们还讨论了当k≥3时的一般调度问题．     

一种新的实时多处理器系统的动态调度算法

实时多处理器系统的动态调度算法一直是实时系统研究中的重要课题,而评价实时调度算法性能的一个最重要的指标是调度成功率.在近视算法的基础上提出了一种新的实时多处理器系统的动态调度算法--节约算法.在该算法中,提出了一个新的处理器选择策略,从而提高了算法的调度成功率.同时,为了研究节约算法的有效性,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,并与近视算法的调度成功率进行了比较.模拟结果显示,节约算法的调度成功率要优于近视算法.     

A Genetic Algorithm for Hybrid Flow-shop Scheduling with Multiprocessor Tasks

The hybrid flow-shop scheduling problem with multiprocessor tasks finds its applications in real-time machine-vision systems among others. Motivated by this application and the computational complexity of the problem, we propose a genetic algorithm in this paper. We first describe the implementation details, which include a new crossover operator. We then perform a preliminary test to set the best values of the control parameters, namely the population size, crossover rate and mutation rate. Next, given these values, we carry out an extensive computational experiment to evaluate the performance of four versions of the proposed genetic algorithm in terms of the percentage deviation of the solution from the lower bound value. The results of the experiments demonstrate that the genetic algorithm performs the best when the new crossover operator is used along with the insertion mutation. This genetic algorithm also outperforms the tabu search algorithm proposed in the literature for the same problem.     

恒速处理机的多机Flow shop最小和调度问题的启发式算法分析

本文研究有n个作业需在5个处理机中心进行加工，处理机中心i由l1个恒速机组成的非抢占式多机flow shop调度最小和问题．每个作业有s个工序，每个工序需在对应的处理机中心的任一台机器上加工处理，作业到达前不能加工，所有作业通过处理机中心的路径相同．目标是确定一个作业在每个处理机中心机器上的可行调度序列，使所有作业在最后处理机中心的加权完成时间总和最小化．在作业处理时间需求、作业权重分别为独立同分布的有界随机变量时，通过特殊flow shop调度松弛方法，我们证明该问题在作业数趋于无穷时，一个基于有效作业最短加权平均处理时间需求的启发式算法是渐近最优的．     

多机Flow shop加权完成时间调度问题的启发式算法分析

因实际生产中调度问题的规模很大，分析其近似算法的绝对性能比很难，有时甚至不可行，所以研究近似算法的渐近性能比就很有必要，本文针对多机Flowshop加权完成时间调度问题，使用单机松弛和概率分析方法，证明了基于加权最短处理时间需求的启发式算法是渐近最优的．     

非同起点加工的多机调度合成算法

针对调度h个独立任务到初始时刻并非都空闲的m台机器上加工，使得机器最长加工时间（makespan）最短的问题，改进MLPT算法以减少运行时间，改进MULTIFIT算法以减少迭代次数，提出以改进的MLPT算法结果为改进的MULTIFIT算法的初始上界的合成算法－CMM，从理论上对MLPT，MULTIFIT和CMM算法的时间复杂度和调度结果进行了分析和比较，实验结果表明：改进的MULTIFIT经MULTIFIT的平均迭代次数少；CMM在平均迭代次数方面甚至比改进的MULTIFIT还少得多且调度结果不次于MULTIFIT和MLPT的优者。     

On the Complexity of Adjacent Resource Scheduling

We study the problem of scheduling resource(s) for jobs in an adjacent manner (ARS). The problem relates to fixed-interval scheduling on one hand, and to the problem of two-dimensional strip packing on the other. Further, there is a close relation with multiprocessor scheduling. A distinguishing characteristic is the constraint of resource-adjacency. As an application of ARS, we consider an airport where passengers check in for their flight, joining lines before one or more desks, at the desk the luggage is checked and so forth. To smoothen these operations the airport maintains a clear order in the waiting lines: a number n(f) of adjacent desks is to be assigned exclusively during a fixed time-interval I(f) to flight f. For each flight in a given planning horizon of discrete time periods, one seeks a feasible assignment to adjacent desks and the objective is to minimize the total number of involved desks. The paper explores two problem variants and relates them to other scheduling problems. The basic, rectangular version of ARS is a special case of multiprocessor scheduling. The other problem is more general and it does not fit into any existing scheduling model. After presenting an integer linear program for ARS, we discuss the complexity of both problems, as well as of special cases. The decision version of the rectangular problem remains strongly NP-complete. The complexity of the other problem is already strongly NP-complete for two time periods. The paper also determines a number of cases that are solvable in polynomial time.     

网络集群计算系统中的并行任务调度

基于多处理机并行任务调度模型,探讨网络集群计算系统中的并行任务调度问题,首先证明了一般网络集群计算系统中调度算法的可近似性难度,然后提出了三种不同的启发式算法：最大长度优先调度算法、最大宽度优先调度算法和最大面积优先调度算法;然后根据大量的模拟实验对这些算法以及文献中已提出的调度算法进行了比较分析,结果表明该文的启发式算法比文献中的算法在性能上效果更好。     

Scheduling parallel jobs to minimize the makespan

We consider the NP-hard problem of scheduling parallel jobs with release dates on identical parallel machines to minimize the makespan. A parallel job requires simultaneously a prespecified, job-dependent number of machines when being processed. We prove that the makespan of any nonpreemptive list-schedule is within a factor of 2 of the optimal preemptive makespan. This gives the best-known approximation algorithms for both the preemptive and the nonpreemptive variant of the problem. We also show that no list-scheduling algorithm can achieve a better performance guarantee than 2 for the nonpreemptive problem, no matter which priority list is chosen. List-scheduling also works in the online setting where jobs arrive over time and the length of a job becomes known only when it completes; it therefore yields a deterministic online algorithm with competitive ratio 2 as well. In addition, we consider a different online model in which jobs arrive one by one and need to be scheduled before the next job becomes known. We show that no list-scheduling algorithm has a constant competitive ratio. Still, we present the first online algorithm for scheduling parallel jobs with a constant competitive ratio in this context. We also prove a new information-theoretic lower bound of 2.25 for the competitive ratio of any deterministic online algorithm for this model. Moreover, we show that 6/5 is a lower bound for the competitive ratio of any deterministic online algorithm of the preemptive version of the model jobs arriving over time.     

三模容错多处理器动态实时调度算法

实时系统现在面临着越来越迫切的容错要求。目前流行的主副备份方式对于任务集有着特殊要求,要求时间限能够允许主副备份串行地执行,并且系统需要提供相应的硬件来检测处理器错误,因此限制了容错的应用范围。本文提出基于三模冗余比较的实时容错算法,采用了副本重载技术和主副本串并行混合调度策略。分析和实验结果表明,该算法具
有更好的适应性。     

Improved Task and Resource Partitioning Under the Resource-Oriented Partitioned Scheduling

Coordinated partitioning and resource sharing have attracted considerable research interest in the field of real-time multiprocessor systems. However, finding an optimal partition is widely known as NP-hard, even for independent tasks. A recently proposed resource-oriented partitioned (ROP) fixed-priority scheduling that partitions tasks and shared resources respectively has been shown to achieve a non-trivial speedup factor guarantee, which promotes the research of coordinated scheduling to a new level. Despite the theoretical elegance, the schedulability performance of ROP scheduling is restricted by the heuristic partitioning methods used in the original study. In this paper, we address the partitioning problem for tasks and shared resources under the ROP scheduling. A unified schedulability analysis framework for the ROP scheduling is proposed in the first place. A sophisticated partitioning approach based on integer linear programming (ILP) is then proposed based on the unified analysis. Empirical results show that the proposed methods improve the schedulability of ROP scheduling significantly, and the runtime complexity for searching a solution is reduced prominently compared with other ILP-based approaches as well.

     

实时多处理机系统BEST-FIT启发式容错调度

本文从有效利用资源的角度出发,提出了一种以最小化处理机数目为优化目标的Best-Fit启发式容错调度算法。该算法采用主/副版本备份技术和副版本的主动运行方式与被运行方式相结合的方法,将实时任务的主版本和副版本调度到不同处理机上运行;并且按照Best-Fit启发式策略为实时任务主版本寻找“最佳满足”处理机,使尽可能多的实时任务副版本以被动方式运行。算法既保证了系统的实时性和容错性,也节约了处理机。分析和仿真结果均证明了算法的有效性。