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1.
With the rapid advance of computing technologies, it becomes more and more common to construct high-performance computing environments with heterogeneous commodity computers. Previous loop scheduling schemes were not designed for this kind of environments. Therefore, better loop scheduling schemes are needed to further increase the performance of the emerging heterogeneous PC cluster environments. In this paper, we propose a new heuristic for the performance-based approach to partition loop iterations according to the performance weighting of cluster/grid nodes. In particular, a new parameter is proposed to consider HPCC benchmark results as part of performance estimation. A heterogeneous cluster and grid were built to verify the proposed approach, and three kinds of application program were implemented for execution on cluster testbed. Experimental results show that the proposed approach performs better than the previous schemes on heterogeneous computing environments.  相似文献   

2.
分布式计算平台中混合多应用调度策略的研究*   总被引:1,自引:0,他引:1  
本文提出与分析了分布式计算平台中几种混合多应用的调度策略,它主要面向多个并行应用之间的调度而不是应用内部的调度,应用内部的调度采用了常见的工作队列容错调度算法。与资源信息有关的调度(Knowledge-Based)比较起来,这些调度策略运用到了与资源信息无关的调度方式(Knowledge-Free),这使它们的实现更加简单与容易,更加适合于高挥发性的分布式计算系统。针对各种不同的计算强度、资源可用性、任务粒度来划分实验场景,把各种调度策略进行了评测与比较。实验结果表明:这些调度策略各有优缺点,可以作为评估大规模分布式计算环境下的并行分布式应用的有效策略。  相似文献   

3.
Loop scheduling on parallel and distributed systems has been thoroughly investigated in the past. However, none of these studies considered the multi-core architecture feature for emerging grid systems. Although there have been many studies proposed to employ the hybrid MPI and OpenMP programming model to exploit different levels of parallelism for a distributed system with multi-core computers, none of them were aimed at parallel loop self-scheduling. Therefore, this paper investigates how to employ the hybrid MPI and OpenMP model to design a parallel loop self-scheduling scheme adapted to the multi-core architecture for emerging grid systems. Three different featured applications are implemented and evaluated to demonstrate the effectiveness of the proposed scheduling approach. The experimental results show that the proposed approach outperforms the previous work for the three applications and the speedups range from 1.13 to 1.75.  相似文献   

4.
We investigate techniques for efficiently executing multiquery workloads from data and computation-intensive applications in parallel and/or distributed computing environments. In this context, we describe a database optimization framework that supports data and computation reuse, query scheduling, and active semantic caching to speed up the evaluation of multiquery workloads. Its most striking feature is the ability of optimizing the execution of queries in the presence of application-specific constructs by employing a customizable data and computation reuse model. Furthermore, we discuss how the proposed optimization model is flexible enough to work efficiently irrespective of the parallel/distributed environment underneath. In order to evaluate the proposed optimization techniques, we present experimental evidence using real data analysis applications. For this purpose, a common implementation for the queries under study was provided according to the database optimization framework and deployed on top of three distinct experimental configurations: a shared memory multiprocessor, a cluster of workstations, and a distributed computational Grid-like environment.  相似文献   

5.
A PTS-PGATS based approach for data-intensive scheduling in data grids   总被引:1,自引:0,他引:1  
Grid computing is the combination of computer resources in a loosely coupled, heterogeneous, and geographically dispersed environment. Grid data are the data used in grid computing, which consists of large-scale data-intensive applications, producing and consuming huge amounts of data, distributed across a large number of machines. Data grid computing composes sets of independent tasks each of which require massive distributed data sets that may each be replicated on different resources. To reduce the completion time of the application and improve the performance of the grid, appropriate computing resources should be selected to execute the tasks and appropriate storage resources selected to serve the files required by the tasks. So the problem can be broken into two sub-problems: selection of storage resources and assignment of tasks to computing resources. This paper proposes a scheduler, which is broken into three parts that can run in parallel and uses both parallel tabu search and a parallel genetic algorithm. Finally, the proposed algorithm is evaluated by comparing it with other related algorithms, which target minimizing makespan. Simulation results show that the proposed approach can be a good choice for scheduling large data grid applications.  相似文献   

6.
基于资源预测的网格任务调度模型   总被引:1,自引:0,他引:1  
程宏兵 《计算机应用》2010,30(9):2530-2534
跨越虚拟组织中多个域(或集群)的网格任务调度由于资源的不确定性(如动态性和异构性)而成为网格应用中亟待解决的问题。提出了一种有效的基于资源预测的网格任务调度模型——RPTS,该模型利用加权最小二乘方法进行参数估计的自回归滑动平均(ARMA)预测方法对网格环境下的主机负载进行预测。利用上述资源预测结果和一类数据并行性网格任务的建模结果,对它们进行预处理、匹配并调度执行。RPTS充分考虑了网格环境下资源的动态性和异构性,为解决网格环境下任务调度问题提供了一种较好的方法。与其他一些网格任务调度方法进行了一系列的仿真实验,结果表明RPTS模型具有任务执行时间最短和稳定性较好的特点。  相似文献   

7.
Workflow scheduling on parallel systems has long been known to be a NP-complete problem. As modern grid and cloud computing platforms emerge, it becomes indispensable to schedule mixed-parallel workflows in an online manner in a speed-heterogeneous multi-cluster environment. However, most existing scheduling algorithms were not developed for online mixed-parallel workflows of rigid data-parallel tasks and multi-cluster environments, therefore they cannot handle the problem efficiently. In this paper, we propose a scheduling framework, named Mixed-Parallel Online Workflow Scheduling (MOWS), which divides the entire scheduling process into four phases: task prioritizing, waiting queue scheduling, task rearrangement, and task allocation. Based on this framework, we developed four new methods: shortest-workflow-first, priority-based backfilling, preemptive task execution and All-EFT task allocation, for scheduling online mixed-parallel workflows of rigid tasks in speed-heterogeneous multi-cluster environments. To evaluate the proposed scheduling methods, we conducted a series of simulation studies and made comparisons with previously proposed approaches in the literature. The experimental results indicate that each of the four proposed methods outperforms existing approaches significantly and all these approaches in MOWS together can achieve more than 20% performance improvement in terms of average turnaround time.  相似文献   

8.
With recent advances in computing and communication technologies enabling mobile devices more powerful, the scope of Grid computing has been broadened to include mobile and pervasive devices. Energy has become a critical resource in such devices. So, battery energy limitation is the main challenge towards enabling persistent mobile grid computing. In this paper, we address the problem of energy constrained scheduling scheme for the grid environment. There is a limited energy budget for grid applications. The paper investigates both energy minimization for mobile devices and grid utility optimization problem. We formalize energy aware scheduling using nonlinear optimization theory under constraints of energy budget and deadline. The paper also proposes distributed pricing based algorithm that is used to tradeoff energy and deadline to achieve a system wide optimization based on the preference of the grid user. The simulations reveal that the proposed energy constrained scheduling algorithms can obtain better performance than the previous approach that considers both energy consumption and deadline.  相似文献   

9.
Distributed object-oriented environments have become important platforms for parallel and distributed service frameworks. Among distributed object-oriented software, .NET Remoting provides a language layer of abstractions for performing parallel and distributed computing in .NET environments. In this paper, we present our methodologies in supporting .NET Remoting over meta-clustered environments. We take the advantage of the programmability of network processors to develop the content-based switch for distributing workloads generated from remote invocations in .NET. Our scheduling mechanisms include stateful supports for .NET Remoting services. In addition, we also propose scheduling policy to incorporate work-flow models as the models are now incorporated in many of tools of grid architectures. The result of our experiment shows that the improvement of EFT is from 5% to 21% when compared to ETT and is from 8% to 34% when compared to RR while the stateful task ratio is 50%. Our schemes are effective in supporting the switching of .NET Remoting computations over meta-cluster environments.  相似文献   

10.
基于学习方式对Hadoop作业调度的改进研究   总被引:1,自引:0,他引:1  
余正样 《计算机科学》2012,39(101):220-222,256
随着并行计算、分布式计算和网格计算技术的发展,云计算作为一种新的模型被提出来,发展极为迅速。Hadoop作为一个开源的云计算系统,得到了广泛的运用。作业调度是Hadoop平台的核心问题之一,通过对Hadoop中已有调度算法的了解和分析后,基于学习的方式,利用过去的节点历史记录和作业属性来不断地改进作业调度;应用了基于特征加权的朴素贝叶斯分类器算法来改进任务的分配调度,并通过实验进行了验证,结果表明它对任务分配调度执行效率有一定的提高。  相似文献   

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