网格计算中一种改进的启发式任务调度算法

网格计算技术是20世纪90年代出现的新兴研究领域.网格系统由异构的资源组成.网格计算中,一个好的任务调度算法不但要考虑所有任务的makespan,使其值尽量小,同样要考虑到整个系统机器间的负载平衡问题.对异构计算环境下的元任务调度算法进行了分析,针对Min-min算法可能引发的负载不平衡问题,结合网格计算环境的特点,提出了一种适用于网格计算环境中的任务调度算法.     

一种基于蚁群算法的任务调度方法

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中任务调度更加复杂。文中结合蚁群算法的优点,提出了一种基于蚁群算法的任务调度方法,将算法应用于网格计算任务调度问题的求解之中。最后,实验结果这种算法优于普通算法。     

网格任务调度机制的研究

由于资源具有异构、动态等特性,计算网格环境下的调度就成了一个非常复杂且具有挑战性的问题.介绍网格任务调度的研究现状,分析网格任务调度算法的基本原理.提出一种网格计算任务调度模型.     

一种新的异构网格任务调度算法

提出了一种基于免疫计算的异构网格任务调度算法。设计了异构网格独立任务调度问题的数学模型,给出了免疫调度算法的框架、基于实数编码的克隆变异算子和浓度抑制算子,并在仿真环境下进行了实验。实验结果表明,算法能有效地解决异构网格任务调度问题,具有较好的应用价值。     

网格计算中任务调度算法的仿真研究

研究网格计算中任务调度优化问题,由于网格环境具有动态性、异构性等特点,对高效调试资源效率有影响,导致传统网格任务调度算法收敛速度慢、局部最优等缺陷,使网格任务调度效率低.为了提高网格任务调度效率,提出一种基于粒子群算法的任务调度模型.模型根据任务调度原理和粒子群算法特点,建立了网格任务调度的元任务模型和性能指标的数学模型,然后采用粒子群算法对该模型进行求解,提高资源利用率和任务执行效率.仿真结果表明,根据粒子群算法的任务调度策略,提高了任务调度的速度和效率,很好的解决网格任务调度中存在的难题.     

基于动态负载均衡策略的网格任务调度优化模型和算法

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中任务调度更加复杂。结合免疫克隆算法和模拟退火算法的优点,提出了一种网格任务调度优化模型和算法。仿真实验结果表明,这种调度算法有效地实现了资源的负载均衡,克服了遗传算法容易陷入局部最优的缺点,可以成功地应用于网格任务调度中。     

基于通信和计算开销的网格任务调度算法的研究

在计算密集型的异构网格环境中,有效的任务调度是一个关键的问题,这是一个完全NP问题,针对这一问题提出了一种基于通信和计算开销的启发式网格任务调度算法,这一算法考虑了不同的节点计算能力、任务大小和网络带宽,最后给出了相应的实验及相关算法的比较结果,表明了该算法对于异构环境具有更优的性能。     

基于Min—Min和蚁群算法的网格任务调度方法

网格任务调度是网格计算的研究热点,也是一个NP难问题。文章结合Min-Min算法和蚁群算法的优点,提出了一种基于Min—Min群算法（MMACO）的任务调度方法。仿真实验表明：在网格环境下,该算法具有较好的全局最优求解能力和较快的收敛速度。     

网格任务调度算法的研究

为了提高网格任务调度算法的性能和效率,同时在任务调度过程中让网格资源的负载达到平衡,通过对网格中三种典型调度算法的原理进行分析研究,结合网格计算环境的特点,针对这些典型算法存在的不足,并以这几个经典的调度算法原理为基础,提出了一种适用于网格计算环境的改进算法.通过对改进的算法进行试验分析,发现改进的算法较好地弥补了传统算法中存在的一些不足.最后提出了笔者对网格环境下任务调度算法的一些见解.     

基于人工鱼群算法的网格任务调度策略

网格任务调度是一个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中任务调度更加复杂。提出了一种基于人工鱼群算法的网络任务调度策略,通过鱼群的觅食、聚群、追尾等方式,实现网格任务的有效调度。     

An ant algorithm for balanced job scheduling in grids

Grid computing utilizes the distributed heterogeneous resources in order to support complicated computing problems. Grid can be classified into two types: computing grid and data grid. Job scheduling in computing grid is a very important problem. To utilize grids efficiently, we need a good job scheduling algorithm to assign jobs to resources in grids.In the natural environment, the ants have a tremendous ability to team up to find an optimal path to food resources. An ant algorithm simulates the behavior of ants. In this paper, we propose a Balanced Ant Colony Optimization (BACO) algorithm for job scheduling in the Grid environment. The main contributions of our work are to balance the entire system load while trying to minimize the makespan of a given set of jobs. Compared with the other job scheduling algorithms, BACO can outperform them according to the experimental results.     

基于任务复制的网格任务调度算法

网格中资源之间存在着通信延迟，通过任务复制的冗余，可以减少任务之间的通信开销，缩短整个计算程序的计算时间。目前网格中的任务调度算法基本上是没有考虑任务复制的；而基于任务复制调度算法往往会产生过多的复制任务，增大系统开销，甚至有可能延迟计算时间。由于基于任务复制的任务调度是一个NP问题，因此本文提出了一种基于任务复制的网格资源调度算法，以减少调度长度为主要目标、减少任务复制量和资源占用量为次要目标。该算法在调度长度和任务复制数量以及占用资源数量方面都等于或优于其它算法。     

基于混合遗传与禁忌搜索的网格任务调度的研究

网格技术将所有可用于共享的资源（例如,计算机、存储设备、软件、数据库等）通过网络连接起来,并将它们转化成一种标准的计算能力。为各种复杂的计算任务提供资源,因此网格任务在各种资源之间的调度成为了一个关键的问题。本文通过对遗传算法与禁忌搜索算法的分析,提出了一种混合遗传算法与禁忌搜索算法的策略,把禁忌搜索算法独有的记忆思想引入到遗传算法的搜索过程中,通过把禁忌搜索算法作为遗传算法的变异算子的方法,提高了网格任务调度的有效性。最后,使用网格模拟器,对算法的性能进行了测试,并比较了轻重负载情况下,经典遗传算法和本算法调度方案的性能差异。     

数据和计算密集混合元任务的网格调度算法

网格计算技术是继Internet计算之后出现的新兴研究领域。网格系统由异构的资源组成，一个好的任务调度方法可以充分利用网格系统的处理能力，减少任务的完成时间。根据目前网格系统的使用模式，提出了符合实际的用户任务形式，即任务由数据传输和计算两部分组成，计算在获得所有输入之后开始执行。多个这样的独立任务组成元任务，作为调度程序的最小执行单位。在实际应用中，元任务应该由数据密集型和计算密集型任务混合组成。考虑到数据传输和计算的比例关系对元任务完成的影响，提出一种新的调度算法TCR，通过提高计算资源的利用率以及任务间的并行度，减少元任务的完成时间。详细介绍了该算法，并通过模拟结果的对比验证了该算法的良好性能。     

基于经济模型的资源代理

网格计算是为解决大规模资源密集型问题而提出的新一代计算平台,是当前并行和分布处理技术的一个发展方向,而资源管理是计算网格的关键技术之一。对各种各样可利用资源的整合和管理是网格应用的基础,而资源的分布性、动态性、异构性、自治性和需要协调一致性使得网格资源的管理调度成为一个棘手的问题。目前基于市场的经济资源管理和调度算法非常适合计算网格中的资源管理问题,但有调度价格不能更改、负载平衡等问题。文中提出了“网格环境下基于经济模型的资源代理”,依靠多维QoS指导的调度策略和经济模型的启发式调节资源价格,改进和优化计算网格资源的分配。     

A PTS-PGATS based approach for data-intensive scheduling in data grids

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.     

浅析网格环境中的任务调度算法

网格系统由大量异构资源组成,具有复杂、动态和自治等特点。高效的调度算法可以充分利用网格系统和处理能力,从而提高应用程序的性能。本文提出Segment Qos Min-Min RR任务调度算法,平衡了负载,提高了任务的完成时间和平均等待时间。     

An agent-based approach for dynamic adjustment of scheduled jobs in computational grids

Grid computing is a newly developed technology for complex systems with large-scale resource sharing, wide-area communication, and multi-institutional collaboration. Grid scheduling is an important infrastructure in the grid computing environment. Most of the existing grids scheduling methods focus on maximizing processor utilization without taking grid load into consideration. This may lead to significant inefficiencies in performance such as large job queues and processing delays. In this paper, we propose a multiagent-based scheduling system for computational grids with a new approach. Agent technology is suitable for a computational grid because of the dynamic, heterogeneous, and autonomous nature of the grid. The main idea of the proposed system is a combination of a static scheduling using a fixed scheduling algorithm and a dynamic adjustment through the autonomous behavior of agents. The superiority of the proposed system, in reducing the load of the grid and minimizing the response time for executing user applications, is demonstrated by simulation experiments.     

计算网格中基于时间均衡的并行粗粒度任务调度算法

考虑网格资源异构、自治、动态等特性,讨论本地用户具有强占优先权情况下的任务调度问题,提出了TBBS(Time-Balancing Based Scheduling Algorithm)算法.建立调度优化模型,以期望完成时间最小为目标选择执行任务的最佳资源组合.以时间均衡策略将任务分解并调度到资源上执行,减少了子任务同步时因等待而产生的延时,获得较好的并行计算性能.采用重复调度策略,适应计算网格中资源的特性.     

Task scheduling using NSGA II with fuzzy adaptive operators for computational grids

Scheduling algorithms have an essential role in computational grids for managing jobs, and assigning them to appropriate resources. An efficient task scheduling algorithm can achieve minimum execution time and maximum resource utilization by providing the load balance between resources in the grid. The superiority of genetic algorithm in the scheduling of tasks has been proven in the literature. In this paper, we improve the famous multi-objective genetic algorithm known as NSGA-II using fuzzy operators to improve quality and performance of task scheduling in the market-based grid environment. Load balancing, Makespan and Price are three important objectives for multi-objective optimization in the task scheduling problem in the grid. Grid users do not attend load balancing in making decision, so it is desirable that all solutions have good load balancing. Thus to decrease computation and ease decision making through the users, we should consider and improve the load balancing problem in the task scheduling indirectly using the fuzzy system without implementing the third objective function. We have used fuzzy operators for this purpose and more quality and variety in Pareto-optimal solutions. Three functions are defined to generate inputs for fuzzy systems. Variance of costs, variance of frequency of involved resources in scheduling and variance of genes values are used to determine probabilities of crossover and mutation intelligently. Variance of frequency of involved resources with cooperation of Makespan objective satisfies load balancing objective indirectly. Variance of genes values and variance of costs are used in the mutation fuzzy system to improve diversity and quality of Pareto optimal front. Our method conducts the algorithm towards best and most appropriate solutions with load balancing in less iteration. The obtained results have proved that our innovative algorithm converges to Pareto-optimal solutions faster and with more quality.