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.     

可重构系统中的实时任务在线调度与放置算法

有效的任务调度与放置是发挥可重构计算性能优势的重要因素.针对实时任务在二维可重构器件上的在线调度问题,定义了调度算法完全识别的概念,即算法不会拒绝能够成功调度的任务.提出了新的实时在线调度与放置算法,充分利用了任务的时间信息,实现了完全识别的调度.实验表明,与已有的算法相比,新算法显著地改善了调度效果,而运行开销没有明显增加.     

Bandwidth‐aware divisible task scheduling for cloud computing

Task scheduling is a fundamental issue in achieving high efficiency in cloud computing. However, it is a big challenge for efficient scheduling algorithm design and implementation (as general scheduling problem is NP‐complete). Most existing task‐scheduling methods of cloud computing only consider task resource requirements for CPU and memory, without considering bandwidth requirements. In order to obtain better performance, in this paper, we propose a bandwidth‐aware algorithm for divisible task scheduling in cloud‐computing environments. A nonlinear programming model for the divisible task‐scheduling problem under the bounded multi‐port model is presented. By solving this model, the optimized allocation scheme that determines proper number of tasks assigned to each virtual resource node is obtained. On the basis of the optimized allocation scheme, a heuristic algorithm for divisible load scheduling, called bandwidth‐aware task‐scheduling (BATS) algorithm, is proposed. The performance of algorithm is evaluated using CloudSim toolkit. Experimental result shows that, compared with the fair‐based task‐scheduling algorithm, the bandwidth‐only task‐scheduling algorithm, and the computation‐only task‐scheduling algorithm, the proposed algorithm (BATS) has better performance. Copyright © 2012 John Wiley & Sons, Ltd.     

预测调度算法在平行机问题中的应用

常用的实时生产调度的在线算法由于只利用当前已到达的工件信息,导致调度性能不够理想。针对复杂度较高的平行机调度问题,通过对在线算法OMPR(单机可中断松弛)的改进,设计了一种具体的预测调度算法PPSA(平行机预测调度算法)。预测调度算法合理地把预知信息与已知信息结合起来进行决策,使调度解的性能得到进一步提高。仿真分析显示,该算法的性能明显优于在线算法OMPR,表明预测调度算法是一种计算简单、性能优良的实时调度算法。     

基于改进遗传算法的智能排课研究

本文深入地分析了排课问题的软约束条件和硬约束条件,抽象出求解智能排课问题的数学模型。深入分析遗传算法,针对传统的遗传算法,对初始种群进行均匀化、适应度函数、变异算子等方面改进。通过对比实验证明改进的算法完全适用于智能排课问题,而且具有较高的效率,为排课问题的发展提供了新的思路。     

A Heuristic Algorithm for Task Scheduling Based on Mean Load on Grid

Efficient task scheduling is critical to achieving high performance on grid computing environment. The task scheduling on grid is studied as optimization problem in this paper. A heuristic task scheduling algorithm satisfying resources load balancing on grid environment is presented. The algorithm schedules tasks by employing mean load based on task predictive execution time as heuristic information to obtain an initial scheduling strategy. Then an optimal scheduling strategy is achieved by selecting two machines satisfying condition to change their loads via reassigning their tasks under the heuristic of their mean load. Methods of selecting machines and tasks are given in this paper to increase the throughput of the system and reduce the total waiting time. The efficiency of the algorithm is analyzed and the performance of the proposed algorithm is evaluated via extensive simulation experiments. Experimental results show that the heuristic algorithm performs significantly to ensure high load balancing and achieve an optimal scheduling strategy almost all the time. Furthermore, results show that our algorithm is high efficient in terms of time complexity.     

基于缓存区段的P2P流媒体调度算法

由于P2P环境的高度动态性和异构性,以及流媒体服务的高宽带和连续性强的特征,P2P流媒体系统中数据块和发送节点的调度便显得尤为重要。本文针对数据驱动的P2P流媒体系统提出了基于缓存区段的调度算法,即根据时间线将缓存划分为反映不同紧急程度的若干区段,以便节点根据资源副本数和紧急程度作出合理调度。模拟实验表明,在高 动态性的网络情况下,基于缓存区段的调度算法仍然能够很好地保证媒体流的连续性以及节点间的协作。     

基于高频车站及时间窗的立体轨道交通系统智能调度算法

立体轨道交通系统的车辆调度方法还未见报道,已有车辆调度算法的实时性较差。针对立体轨道交通车辆的调度问题,研究了一种结合高、低频车站判定的订单分配算法和一种结合时间窗的Dijkstra路径规划算法,即智能调度算法,以提高车辆的运行效率。首先,使用订单分配算法为订单选择合适的执行车辆,减少乘客的等待时间。其次,在订单分配算法的基础上增加了高、低频车站的判定,提前给高频车站调度车辆,以保证供需平衡。然后,将普通Dijkstra算法和时间窗判断相结合,以实现多车辆的无冲突路径规划。最后,对OpenTCS软件进行二次开发,并进行了调度算法的仿真。结果表明,当有乘客叫车时,若只有订单分配算法,乘客平均等待时间为8.043 s;结合高、低频车站进行车辆提前调度后,平均等待时间降到了5.724 s,每位乘客减少了2.319 s的等待时间。路径规划时,无论是普通的Dijkstra算法还是结合时间窗的Dijkstra算法,规划耗时都在1 ms以内,而结合时间窗的Dijkstra算法在只增加约0.1 ms耗时的情况下,解决了车辆的路径冲突问题。研究的智能调度算法减少了乘客的等待时间,提高了车辆的运行效率,实时性好,能满足立体轨道交通车辆的调度要求。     

并行任务图的优化调度算法

科学与工程计算中的很多复杂应用问题需要使用科学工作流技术,超算领域中的科学工作流常以并行任务图建模,并行任务图的有效调度对应用的高效执行有重要意义。给出了资源限制条件下并行任务图的调度模型;针对Fork-Join类并行任务图给出了若干最优化调度结论;针对一般并行任务图提出了一种新的调度算法,该算法考虑了数据通信开销对资源分配和调度性能的影响,并对已有的CPA算法在特定情况下进行了改进。通过实验与常用的CPR和CPA算法做比较,验证了提出的新算法能够获得很好的调度效果。本文提出的调度算法和得到的最优调度结论对工作流应用系统的高性能调度功能开发具有借鉴意义。     

Rolling-horizon scheduling for energy constrained distributed real-time embedded systems

Energy-efficient scheduling approaches are critical to battery driven real-time embedded systems. Traditional energy-aware scheduling schemes are mainly based on the individual task scheduling. Consequently, the scheduling space for each task is small, and the schedulability and energy saving are very limited, especially when the system is heavily loaded. To remedy this problem, we propose a novel rolling-horizon (RH) strategy that can be applied to any scheduling algorithm to improve schedulability. In addition, we develop a new energy-efficient adaptive scheduling algorithm (EASA) that can adaptively adjust supply voltages according to the system workload for energy efficiency. Both the RH strategy and EASA algorithm are combined to form our scheduling approach, RH-EASA. Experimental results show that in comparison with some typical traditional scheduling schemes, RH-EASA can achieve significant energy savings while meeting most task deadlines (namely, high schedulability) for distributed real-time embedded systems with dynamic workloads.     

最短响应时间优先请求分配算法的研究

分析了在分布式高性能防火墙中两种常用的请求分配算法,在此基础上提出了最短响应时间优先调度算法。仿真表明,该算法具有很好的调度效果和很高的稳定性。     

基于改进遗传算法的网格任务调度研究

网格任务调度是一个NP完全问题,它关注大规模的资源和任务调度,要求采用具有高效性的调度算法.提出了一种基于改进遗传算法的网格任务调度算法,在算法初始化种群产生时引入min-min算法和max-min算法,从而提高初始化种群的质量;算法迭代过程中采用了一种新的局部收敛判断以及改进的变异操作来防止局部收敛.仿真结果表明,该改进算法能更有效地解决网格任务调度问题.     

面向边缘计算的Storm边缘节点调度优化方法

边缘计算有高实时性和大数据交互处理的需求,边缘异构节点间的调度时耗长、通信时延高以及负载不均衡是影响边缘计算性能的核心问题,传统的云计算平台难以满足新的要求。文中研究了在边缘计算环境下Storm边缘节点的调度优化方法,建立了面向边缘计算的Storm任务卸载调度模型。针对拓扑任务在边缘异构节点间的实时动态分配问题,提出了一种启发式动态规划算法(Inspire Dynamic Programming,IDP),通过改变Storm的Task实例的排序分配方式以及Task实例和Slot任务槽的映射关系实现全局的优化调度;同时,针对拓扑任务的并发度受限于JVM栈深度的缺陷,提出了一种基于蝙蝠算法的调度策略。实验结果表明,与Storm调度算法相比,所提算法在边缘节点CPU利用率指标上平均提升了约60%,在集群的吞吐量指标上平均提升了约8.2%,因此能够满足边缘节点之间的高实时性处理要求。     

基于任务分配与调度的GSAT算法求解3-SAT问题

基于不同分配策略的云计算任务调度以及任务分配与调度的主要目的,提出了一种新的算法—求解3-SAT问题的基于任务分配与调度的GSAT算法。该算法将3-SAT问题中的每一个变量形成一个任务,在GSAT算法的基础上,引入任务分配与调度指导贪心搜索;同时,在保留原有贪心搜索的前提下,根据任务分配与调度的思想和3-SAT问题的特点,设计了两种新的策略—分配策略和调度策略共同完成整个贪心搜索过程。以标准的SATLAB库中变量个数从 20～250的3 700个不同规模的标准Uniform Random 3-SAT 问题对新的算法的性能进行了合理的测试,并与高效和普通性能改进的GSAT算法的结果作了比较,结果表明,该算法具有更高的成功率和更少的翻转次数。     

一种新型实时调度算法研究

在许多片上特定应用系统中，任务多且切换频繁，任务切换开销大，有时甚至严重影响系统的可调度性．研究了动态可抢占门限调度算法，它通过初始门限值、动态门限值的计算和优化线程分配，实现了在处理器高利用率下，有效降低任务切换开销的目的，并相应地减少了对内存的需求．动态可抢占门限调度算法是将静态抢占门限算法与动态调度算法有机地结合在一起。完成了由静态到动态无缝转换．     

独立任务调度的启发式算法

任务调度是一个NP-hard问题,而且是并行与分布式计算中一个必不可少的组成部分,特别是在网格计算环境下任务调度更加复杂。该文提出了满足负载均衡的一个启发式任务调度算法。给出了选择处理机和任务的方法,以提高算法的效率。实验表明该算法是一个高效率的调度算法,并且几乎总是找到了最优调度方案。     

Genetic algorithm-based neural fuzzy decision tree for mixed scheduling in ATM networks

Future broadband integrated services networks based on asynchronous transfer mode (ATM) technology are expected to support multiple types of multimedia information with diverse statistical characteristics and quality of service (QoS) requirements. To meet these requirements, efficient scheduling methods are important for traffic control in ATM networks. Among general scheduling schemes, the rate monotonic algorithm is simple enough to be used in high-speed networks, but does not attain the high system utilization of the deadline driven algorithm. However, the deadline driven scheme is computationally complex and hard to implement in hardware. The mixed scheduling algorithm is a combination of the rate monotonic algorithm and the deadline driven algorithm; thus it can provide most of the benefits of these two algorithms. In this paper, we use the mixed scheduling algorithm to achieve high system utilization under the hardware constraint. Because there is no analytic method for schedulability testing of mixed scheduling, we propose a genetic algorithm-based neural fuzzy decision tree (GANFDT) to realize it in a real-time environment. The GANFDT combines a GA and a neural fuzzy network into a binary classification tree. This approach also exploits the power of the classification tree. Simulation results show that the GANFDT provides an efficient way of carrying out mixed scheduling in ATM networks.     

基于关键路径链和PACGA的网络总线资源调度方法研究

针对传统工业控制网络总线资源调度算法在节点数量逐渐增加时收敛速度慢和搜索精度不高,且准确度及效率低等问题,提出了一种基于关键路径链和多态蚁群遗传算法(PACGA)的资源调度方法,采用关键路径链的调度算法获取需求调度的节点,不同节点间采用多态蚁群遗传算法进行资源的调度,依据照工业控制网络资源调度的特征,用自适应调整挥发系数增强节点的全局搜索性能,通过候选节点集方法缩小搜索区域提高算法的搜索效率,完成工业控制网络总线资源的高效调度;仿真实验说明,该种方法在工业控制过程中任务数量较多的情况下仍然具备较高的运行效率和精度,并且具有较低的运行时间,具有较强的应用价值。     

基于混合粒子群算法的云计算任务调度研究

任务调度是云计算系统可靠运行的关键,云计算环境中要处理的任务量巨大,考虑到云计算任务调度和QoS的优化问题,提出一种混合粒子群优化算法用于云任务调度。算法中引入遗传算法的交叉和变异思想,并结合随迭代次数变化的变异指数,保证种群进化初期具有较高的全局搜索能力,避免出现"早熟",同时将爬山算法引入粒子群算法,改善局部搜索能力。实验结果显示该算法具有很好的寻优能力,是一种有效的云计算任务调度算法。     

ACOCS:一种云环境下资源调度混合算法

蚁群算法在优化组合问题中有着重要的意义,传统的蚁群调度算法搜索速度慢、容易陷入局部最优。针对这种情况,结合布谷鸟搜索算法,提出一种基于蚁群算法与布谷鸟搜索算法的混合算法(ACOCS),用于云环境下的资源调度。该方法有效保留了蚁群算法求解精度高和鲁棒性的特性,并融入了布谷鸟搜索具有快速全局搜索能力的优势。仿真实验结果表明,提出的ACOCS调度算法有效减少了调度所需的响应时间,也在一定程度上提高了系统资源利用率。