实时环境下的问题求解*

实时环境下的问题求解是近年来规划问题研究感兴趣的话题.在讨论了传统规划算法的不足之后,引入了在实时环境下求解问题的方法——任意时间算法.任意时间算法可以合理分配时间资源,保证系统最佳的输出性能;同时,任意时间算法可以在任意时刻中断,并输出当时相对最优可行解.遗传算法具有任意时间算法的特性,在介绍了同其他搜索算法的不同之处后,通过实验得出利用随机搜索技术和知识指导相结合的方法,可以较好地处理实时规划问题.最后给出结论,并且简单地讨论了实时规划算法求解问题的策略,同时讨论了今后的发展方向.     

红外低轨星座多目标传感器调度研究

关于卫星多目标传感器调度问题,由于资源有限,实时调度很难解决.为解决上述问题,保证长时间目标接力跟踪和并行多任务处理,提高实时调度能力,通过详细定义和建立数学模型,提出了自适应多任务规划概率选择算法,用趋势捕捉、概率选择和克隆变异防止陷入局部最优.采用启发规则避免了进化算法的盲目性.进行仿真的结果表明,算法能更好地解决多个优化目标及多个任务条件下传感器实时调度问题.     

数据流QoS自适应框架调度算法的研究与实现

在数据流QoS自适应框架中,针对如何为具有截止期和重要性两个特征参数的流数据分派优先级,在讨论了了当前调度算法优缺点的基础上,提出了称为截止期-重要性优先DIF（deadline-importance first）的实时调度算法,并利用链表给出了算法的实现.实验结果表明,在所有负载条件下DIF算法相对于EDF（earliest deadline first）算法、HVF（highest value first）算法和CDF（criticalness-deadline first）算法,在性能方面具有明显改善,特别在系统过载的情况下,能够优雅地降级.     

蚁群优化算法在物流车辆调度系统中的应用

根据对蚁群算法进行的深入研究,指出了蚁群算法在解决大型非线性系统优化问题时的优越性。通过仔细分析遗传算法和粒子群算法在解决物流车辆调度系统问题的不足之处,基于蚁群算法的优点,并根据物流车辆调度系统自身的特点,对基本蚁群算法进行适当的改进,给出算法框架。并且以线性规划理论为基础,建立物流车辆系统的数学模型,给出调度目标与约束条件,用改进后的蚁群算法求解物流车辆调度系统的问题,求得最优解,根据最优解和调度准则进行实时调度。使用Java语言编写模拟程序对比基于改进粒子群算法和改进蚁群算法的调度程序。通过对比证明了所提出的改进蚁群算法解决物流车辆调度优化问题的正确性和有效性     

基于反馈控制的实时调度算法设计与实现

传统的实时调度算法在运行环境不可预测的嵌入式操作系统中应用时,要求系统预留大量的CPU资源,而且在稳定性和精确性等方面存在不足.文章为解决这些问题提出了基于反馈控制的实时调度算法,仿真表明该算法相对传统算法而言,提高了系统的CPU利用率,并降低了任务的截止期限错过率.     

基于(m,k)-firm约束规范的混合任务调度算法研究

讨论了在准实时环境下,包括准实时周期任务和准实时非周期任务在内的混合任务调度算法HTSF.HTSF算法是在满足周期任务(m,k)-firm 约束规范的前提下提高非周期任务可调度性,同时合理利用可用空闲时间,提高整个系统的服务质量.HTSF算法给出了非周期任务的可调度性分析方法,同时采用静态调度与动态调度相结合的方法调度周期任务和非周期任务.模拟测试结果显示,系统对非周期任务的接收率比同类相关算法的接收率高.     

通用调度框架的改进及其在Linux中的实现

在嵌入式Linux实时系统中,要求内核对不同时问约束的任务采用不同的调度算法.但目前Linux内核采用单一的实时调度模式,不能灵活地执行多种调度算法,也就无法满足实时系统中实时任务的时间约束.引入了一种能够在Linux内核调度中执行多种调度算法的框架,即通用调度框架(GSF),并改进了其中的多算法调用机制,从而更好地在Linux内核中实现GSF.     

一个多处理器实时线程调度算法及其在MACH操作系统下的实现方法研究

在实时操作系统中，任务调度在处理器资源的管理中起着十分关键的作用。本文提出了一种基于线程的、动态的、非抢占的多处理器实时任务调度算法，该算法可以高效地在多处理器系统上同时进行周期性和非周期性实时线程的调度。本文还讨论了该算法在ＭＡＣＨ操作系统环境下的实现方法。     

嵌入式实时调度框架研究

嵌入式实时操作系统环境中的多类型实时任务并存的情况给实时调度机制带来了新的需求和挑战。提出了一种适用于嵌入式实时环境的调度框架,可以调度多类型实时和非实时任务,且可通过自适应控制来调节软实时任务带宽,使系统实时性最优化。它引入了两个层次的任务准入控制,解决了任务相关性和资源等约束问题,保证了进入系统的任务独立性和可调度性。它具有良好的可扩展性和可配置性,适用于分布式实时环境。     

蚁群算法在服务器集群批量任务调度中的应用

服务器集群中的负载均衡和作业调度是影响系统性能的重要因素.本文描述服务器集群批量任务的作业调度问题,对该问题建立了基于图的模型.由于使用一般的启发式算法或动态规划算法解决该问题具有局限性,本文引入蚁群算法进行求解,并针对该问题具体求解提出了启发式距离合适的计算方法.最后在仿真的基础上,讨论了算法的优化效果和收敛性,结果表明蚁群算法解决该问题具有优异的性能.     

A cutting plane approach for integrated planning and scheduling

In this paper we propose a branch-and-cut algorithm for solving an integrated production planning and scheduling problem in a parallel machine environment. The planning problem consists of assigning each job to a week over the planning horizon, whereas in the scheduling problem those jobs assigned to a given week have to be scheduled in a parallel machine environment such that all jobs are finished within the week. We solve this problem in two ways: (1) as a monolithic mathematical program and (2) using a hierarchical decomposition approach in which only the planning decisions are modeled explicitly, and the existence of a feasible schedule for each week is verified by using cutting planes. The two approaches are compared with extensive computational testing.     

基于对象分布式实时系统约束的一致性研究

在分布式实时系统中,时间约束规格的一致性是解决任务分配和调度等关键问题的必要前提。该文给出了一种基于对象分布式实时系统调度的通用模型,并对该模型进行了形式化描述。该模型克服了以往模型不能在应用系统的逻辑和功能部件上描述系统实时约束的不足,允许从方法和活动上描述所需的约束,降低了单一约束描述的繁杂程度。为了解决使用该模型进行约束规格的一致性问题,该文给出了绝对时间约束、相对时间约束、一致性约束以及相对时间约束和一致性约束之间的一致性判定的必要条件。     

Energy-aware disk scheduling for soft real-time I/O requests

In this work, we develop energy-aware disk scheduling algorithm for soft real-time I/O. Energy consumption is one of the major factors which bar the adoption of hard disk in mobile environment. Heat dissipation of large scale storage system also calls for an energy-aware scheduling technique to further increase the storage density. The basic idea in this work is to properly determine the I/O burst size so that device can be in standby mode between consecutive I/O bursts and that it can satisfy the soft real-time requirement. We develop an elaborate model which incorporates the energy consumption characteristics, overhead of mode transition in determining the appropriate I/O burst size and the respective disk operating schedule. Efficacy of energy-aware disk scheduling algorithm greatly relies on not only disk scheduling algorithm itself but also various operating system and device firmware related concerns. It is crucial that the various operating system level and device level features need to be properly addressed within disk scheduling framework. Our energy-aware disk scheduling algorithm successfully addresses a number of outstanding issues. First, we examine the effect of OS and hard disk firmware level prefetch policy and incorporate its effect in our disk scheduling framework. Second, our energy aware scheduling framework can allocate a certain fraction of disk bandwidth to handle sporadically arriving non real-time I/O’s. Third, we examine the relationship between lock granularity of the buffer management and energy consumption. We develop a prototype software with energy-aware scheduling algorithm. In our experiment, proposed algorithm can reduce the energy consumption to one fourth if we use energy-aware disk scheduling algorithm. However, energy-aware disk scheduling algorithm increases buffer requirement significantly, e.g., from 4 to 140 KByte. We carefully argue that the buffer overhead is still justifiable given the cost of DRAM chip and importance of energy management in modern mobile devices. The result of our work not only provides the energy efficient scheduling algorithm but also provides an important guideline in capacity planning of future energy efficient mobile devices. This paper is funded by KOSEF through Statistical Research Paper for Complex System at Seoul National University.     

Bacterial memetic algorithm for simultaneous optimization of path planning and flow shop scheduling problems

The paper deals with simultaneous optimization of path planning of mobile robots and flow shop scheduling problem. The goal of the path planning problem is to determine an optimal collision-free path between a start and a target point for a mobile robot in an environment surrounded by obstacles. The objective is to minimize the path length without colliding with an obstacle. On the other hand, shop scheduling problems deal with processing a given set of jobs on a given number of machines. Each operation has an associated machine on which it has to be processed for a given length of time. The problem is to minimize the overall time demand of the whole process. In this paper, we deal with two robots carrying items between the machines. Bacterial memetic algorithm is proposed for solving this combined problem. The algorithm is verified by experimental simulations and compared to classical techniques.     

Dispatching rule selection using artificial neural networks for dynamic planning and scheduling

To schedule a job shop, the first task is to select an appropriate scheduling algorithm or rule. Because of the complexity of scheduling problems, no general algorithm sufficient for solving all scheduling problems has yet been developed. Most job-shop scheduling systems offer alternative algorithms for different situations, and experienced human schedulers are needed to select the best dispatching rule in these systems. This paper proposes a new algorithm for job-shop scheduling problems. This algorithm consists of three stages. First, computer simulation techniques are used to evaluate the efficiency of heuristic rules in different scheduling situations. Second, the simulation results are used to train a neural network in order to capture the knowledge which can be used to select the most efficient heuristic rule for each scheduling situation. Finally, the trained neural network is used as a dispatching rule selector in the real-time scheduling process. Research results have shown great potential in using a neural network to replace human schedulers in selecting an appropriate approach for real-time scheduling. This research is part of an ongoing project of developing a real-time planning and scheduling system.     

Multi-agent based approach for single machine scheduling with sequence-dependent setup times and machine maintenance

Scheduling of single machine in manufacturing systems is especially complex when the order arrivals are dynamic. The complexity of the problem increases by considering the sequence-dependent setup times and machine maintenance in dynamic manufacturing environment. Computational experiments in literature showed that even solving the static single machine scheduling problem without considering regular maintenance activities is NP-hard. Multi-agent systems, a branch of artificial intelligence provide a new alternative way for solving dynamic and complex problems. In this paper a collaborative multi-agent based optimization method is proposed for single machine scheduling problem with sequence-dependent setup times and maintenance constraints. The problem is solved under the condition of both regular and irregular maintenance activities. The solutions of multi-agent based approach are compared with some static single machine scheduling problem sets which are available in the literature. The method is also tested under real-time manufacturing environment where computational time plays a critical role during decision making process.     

一种MapReduce实时调度算法设计及实现

MapReduce是云计算中重要的批数据处理框架,多任务共享MapReduce机群并满足任务实时性要求是调度算法急需解决的问题。提出两阶段实时调度算法,将调度划分为任务间调度和任务内调度。对于任务间调度,使用抽样法和经验值法确定子任务执行时间,利用该参数建立资源分配模型,动态确定任务优先级进行调度;对于子任务使用延迟调度策略进行调度,保证计算的本地性。实验结果显示,两阶段实时调度算法相比公平调度算法和FIFO算法,在保证吞吐量的同时能够满足任务实时性要求。     

一种开放混合实时系统的开放自适应调度算法

开放计算环境下的实时与非实时任务不确定并发,以及多种实时约束混合的复杂约束系统,即开放混合实时系统的需求越来越广泛.通过引入接收控制、调度服务器、自适应调节机制,提出一种开放环境下的自适应实时系统调度架构--OARtS(open adaptive real-time scheduling).它能适应开放计算环境的不确定性,有控制地接受实时任务运行;可根据系统空闲计算带宽变化,自适应地调节任务的实时等级,使得系统运行在最优的实时性能上;对于软实时任务,可根据其计算带宽需求变化,自适应地调节其计算带宽分配,以适应任务执行时间时变引起的实时不确定性.     

上海网格中间件中基于负载平衡的调度框架

在网格环境中,实现动态的负载平衡在服务调度中起到了非常关键的作用。然而,网格环境中的资源的动态性决定了它难以被监控,自治性又决定了它难以被集中管理。为在网格中间件中实现服务调度的负载平衡,提供了一些具有参考价值的解决方案。介绍设计和实现调度框架的动机以及需要解决的问题,并深入探讨在上海网格中间件中设计和实现调度框架的思路,以及达到动态负载平衡的具体方案。     

基于GIS技术和加权kNN算法的实时揽件调度方法

在物流末端的配送服务中,实时揽件调度研究尚处于空白。基于GIS技术、Web技术和移动开发技术,构建了针对“最后一公里”配送的智能物流信息系统。在此系统框架内,改进加权kNN分类算法实现快递人员的实时揽件调度。通过在菜鸟驿站某网点配送活动中的应用,表明智能物流信息系统能有效提升物流网点的服务质量,实时揽件调度方法也对解决实际问题效果显著。