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针对网格任务调度的动态特性,提出一种改进的遗传算法——动态遗传算法(DGA),设计了新的编码机制和适应度函数,以及相应的选择、交叉和变异算子。根据网格系统各服务节点的计算能力、负载及网络状态进行动态调度,不仅使总的完成时间最短,尽量使主机的空闲时间最短,同时满足每个任务的截止时间的要求。在OPNET环境中构建了一个局部网格仿真模型,对所提出的动态遗传算法进行了仿真实验,并与其他常见网格任务调度算法进行了对比,结果表明动态遗传算法具有很好的优化能力,提供了较好的服务质量。 相似文献
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现有的跨自治域网格任务调度算法均使用固定数目的任务备份来提高任务调度的成功率和容错性,无法适应网格环境动态性的特点.提出了三种基于自适应备份数并考虑网格安全因素的任务调度算法,分别为简单自适应备份算法、最高百分之K备份算法和懒惰备份算法.自适应备份算法根据整个网格系统的安全状况,自适应调整需备份的任务及任务备份数,并对失败的任务重新调度.仿真结果表明,基于自适应备份的网格任务调度算法可以有效提高不安全网格环境下的任务调度成功率,具有很好的容错性和可扩展性,优于固定备份数的任务调度算法. 相似文献
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提出了一种基于动态粒子群优化的网格任务调度算法。设计了网格任务调度问题的数学模型,给出了自适应变异的动态粒子群优化算法的框架,引入了自适应学习因子和自适应变异策略,从而使算法具有动态自适应性,能够较容易地跳出局部最优。实验结果表明,本文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。 相似文献
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高效的动态任务调度和容错机制是高性能计算面临的挑战之一,已有的方法难以高效扩展到大规模环境.针对该问题,提出了基于N层排队理论的高可扩展动态任务调度模型,为程序员提供简洁的并行编程框架,有效降低了编程负担;使用泊松过程相关理论分析了任务申请的平均等待时间,通过给定的阈值进行决策分层;结合局部感知的轻量级降级模型,可有效降低大规模并行课题的容错开销,提高系统的可用性.Micro Benchmark在神威蓝光32768核环境下测试表明,对于平均执行时间为3.4s的短任务,基于N层排队理论的动态任务调度模型可扩展性很好,调度开销是传统模型的7.2%;药物软件DOCK在16384核环境下的整体性能比该软件原有的任务调度提升34.3%;局部感知的轻量级降级模型具有故障后损失小的特点,DOCK的测试表明比传统容错方法执行时间减少3.75%~5.13%. 相似文献
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将遗传算法应用于网格任务调度系统中,以实现对任务调度方案的优化。提出了一个使网格执行总任务的最大完成时间最小的优化目标函数。并使用MATLAB完成对谊算法的仿真。 相似文献
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通过对数据网格模型及任务调度过程进行分析,归纳了数据网格任务调度流程,定义了数据网格的任务执行时间和执行花费。对网格模拟器GridSim进行扩展,增加了数据网格任务调度的模拟功能,介绍了扩展后的模拟器体系结构、工作流程和关键技术。通过实验表明,该任务调度模拟器可以满足数据网格优化理论研究的需要,能够对任务调度策略的性能进行比较。 相似文献
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基于效益函数的网格任务调度算法 总被引:1,自引:0,他引:1
在动态、异构、分布广泛的网格环境中,对资源的调度是一个非常复杂而重要且具有挑战性的问题。本文针对网格环境中的动态性特点,特别是用户QoS要求的动态变化性,提出了一种基于效益函数的网格任务调度算法,并采用GridSim模拟器分别对该调度算法和模拟器自带的代价最优和时间最优的网格任务调度算法进行模拟。实验的结果表明:该调度算法更能体现用户对QoS要求的动态变化;在系统完成相同数量的网格任务时,消耗相同时间的情况下,该调度算法在代价上优于基于时间优化的调度算法;而花费相同预算的情况下,在时间上优于基于代价优化的调度算法。 相似文献
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可靠的网格作业调度机制 总被引:1,自引:1,他引:0
针对网格环境的动态性特征,提出了一种可靠的网格作业调度机制(DGJS)。按照作业完成时间期限,DGJS将作业分为:高QoS级、低QoS级和无QoS级,不同QoS级作业有不同的调度优先权;基于资源可用性预测,DGJS采用基于可靠性代价的作业调度策略,将作业尽可能调度到可靠性高的资源节点;另外,DGJS对不同QoS级作业采用不同的容错策略,在保证故障容错的同时,节省网格资源。实验表明:在动态的网格环境下,较之传统的网格作业调度算法,DGJS提高了作业成功率,减少了作业完成时间。 相似文献
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Najme MANSOURI 《Frontiers of Computer Science in China》2014,(3):391-408
Data Grid integrates graphically distributed resources for solving data intensive scientific applications. Effective scheduling in Grid can reduce the amount of data transferred among nodes by submitting a job to a node, where most of the requested data files are available. Scheduling is a traditional problem in parallel and distributed system. However, due to special issues and goals of Grid, traditional approach is not effective in this environment any more. Therefore, it is necessary to propose methods specialized for this kind of parallel and distributed system. Another solution is to use a data replication strategy to create multiple copies of files and store them in convenient locations to shorten file access times. To utilize the above two concepts, in this paper we develop a job scheduling policy, called hierarchical job scheduling strategy (HJSS), and a dynamic data replication strategy, called advanced dynamic hierarchical replication strategy (ADHRS), to improve the data access efficiencies in a hierarchical Data Grid. HJSS uses hierarchical scheduling to reduce the search time for an appropriate computing node. It considers network characteristics, number of jobs waiting in queue, file locations, and disk read speed of storage drive at data sources. Moreover, due to the limited storage capacity, a good replica replacement algorithm is needed. We present a novel replacement strategy which deletes files in two steps when free space is not enough for the new replica: first, it deletes those files with minimum time for transferring. Second, if space is still insufficient then it considers the last time the replica was requested, number of access, size of replica and file transfer time. The simulation results show that our proposed algorithm has better performance in comparison with other algorithms in terms of job execution time, number of intercommunications, number of replications, hit ratio, computing resource usage and storage usage. 相似文献
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Najme MANSOURI 《Frontiers of Computer Science》2014,8(3):391-408
Data Grid integrates graphically distributed resources for solving data intensive scientific applications. Effective scheduling in Grid can reduce the amount of data transferred among nodes by submitting a job to a node, where most of the requested data files are available. Scheduling is a traditional problem in parallel and distributed system. However, due to special issues and goals of Grid, traditional approach is not effective in this environment any more. Therefore, it is necessary to propose methods specialized for this kind of parallel and distributed system. Another solution is to use a data replication strategy to create multiple copies of files and store them in convenient locations to shorten file access times. To utilize the above two concepts, in this paper we develop a job scheduling policy, called hierarchical job scheduling strategy (HJSS), and a dynamic data replication strategy, called advanced dynamic hierarchical replication strategy (ADHRS), to improve the data access efficiencies in a hierarchical Data Grid. HJSS uses hierarchical scheduling to reduce the search time for an appropriate computing node. It considers network characteristics, number of jobs waiting in queue, file locations, and disk read speed of storage drive at data sources. Moreover, due to the limited storage capacity, a good replica replacement algorithm is needed. We present a novel replacement strategy which deletes files in two steps when free space is not enough for the new replica: first, it deletes those files with minimum time for transferring. Second, if space is still insufficient then it considers the last time the replica was requested, number of access, size of replica and file transfer time. The simulation results show that our proposed algorithm has better performance in comparison with other algorithms in terms of job execution time, number of intercommunications, number of replications, hit ratio, computing resource usage and storage usage. 相似文献
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Javid Taheri Young Choon Lee Albert Y. Zomaya Howard Jay Siegel 《Computers & Operations Research》2013
This paper presents a novel Bee Colony based optimization algorithm, named Job Data Scheduling using Bee Colony (JDS-BC). JDS-BC consists of two collaborating mechanisms to efficiently schedule jobs onto computational nodes and replicate datafiles on storage nodes in a system so that the two independent, and in many cases conflicting, objectives (i.e., makespan and total datafile transfer time) of such heterogeneous systems are concurrently minimized. Three benchmarks – varying from small- to large-sized instances – are used to test the performance of JDS-BC. Results are compared against other algorithms to show JDS-BC's superiority under different operating scenarios. These results also provide invaluable insights into data-centric job scheduling for grid environments. 相似文献
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计算网格中动态负载平衡的分布调度模式 总被引:1,自引:0,他引:1
网格计算下对资源进行有效的管理和调度可以提高系统的利用率.在对现有若干调度方法的研究和分析基础上,针对计算网格中的负载平衡问题,提出了一种分布式网格作业调度模型,并给出相关算法.算法通过建立主从模式的负载信息收集机制,提供给节点全局负载信息,加速重负载节点的负载转移速度.通过有效的负载平衡模式,解决资源调度中负载平衡及其可靠性问题. 相似文献
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基于层次化调度策略和动态数据复制的网格调度方法 总被引:2,自引:0,他引:2
针对在网格中如何有效地进行任务调度和数据复制, 以便减少任务执行时间等问题, 提出了任务调度算法(ISS)和优化动态数据复制算法(ODHRA), 并构建一个方案将两种算法进行了有效结合。该方案采用ISS算法综合考虑任务等待队列的数量、任务需求数据的位置和站点的计算容量, 采用网络结构分级调度的方式, 配以适当的权重系数计算综合任务成本, 搜索出最佳计算节点区域; 采用ODHRA算法分析数据传输时间、存储访问延迟、等待在存储队列中的副本请求和节点间的距离, 在众多的副本中选取出最佳副本位置, 再结合副本放置和副本管理, 从而降低了文件访问时间。仿真结果表明, 提出的方案在平均任务执行时间方面, 与其他算法相比表现出了更好的性能。 相似文献
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Shunmei Meng Qianmu Li Taoran Wu Weijia Huang Jing Zhang Weimin Li 《Computational Intelligence》2019,35(3):577-598
Mobile edge cloud computing has been a promising computing paradigm, where mobile users could offload their application workloads to low‐latency local edge cloud resources. However, compared with remote public cloud resources, conventional local edge cloud resources are limited in computation capacity, especially when serve large number of mobile applications. To deal with this problem, we present a hierarchical edge cloud architecture to integrate the local edge clouds and public clouds so as to improve the performance and scalability of scheduling problem for mobile applications. Besides, to achieve a trade‐off between the cost and system delay, a fault‐tolerant dynamic resource scheduling method is proposed to address the scheduling problem in mobile edge cloud computing. The optimization problem could be formulated to minimize the application cost with the user‐defined deadline satisfied. Specifically, firstly, a game‐theoretic scheduling mechanism is adopted for resource provisioning and scheduling for multiprovider mobile applications. Then, a mobility‐aware dynamic scheduling strategy is presented to update the scheduling with the consideration of mobility of mobile users. Moreover, a failure recovery mechanism is proposed to deal with the uncertainties during the execution of mobile applications. Finally, experiments are designed and conducted to validate the effectiveness of our proposal. The experimental results show that our method could achieve a trade‐off between the cost and system delay. 相似文献