任务调度论文：任务调度粗粒度并行遗传算法自适应免疫机制GridSim

1、【关键词】任务调度粗粒度并行遗传算法自适应免疫机制购买全文在线加 QQ 13993 8848 即付即发同时提供论文写作指导和学术期刊发表服务在:本立为学术立献总库合作提供I如有异溟请与总库或学校联系0GridSim【英文关键词】 Task Scheduli ngCoarse-gra inedParallel Gen etic AlgorithmAdaptive Immune Mecha nismGridSim任务调度论文：基于改进进化算法的网格任务调度研究与实现【中文摘要】网格系统是目前应用最广泛的分布式应用系统，它利用 In ternet 将世界上不同地域内的资源整合成一个虚拟的有机体 类似

2、于一个逻辑整体或一台超级计算机，用户从中不仅可以享受一体 化的信息和应用服务，还可以共享资源和协同工作。网格中不存在资 源“孤岛”，它实现了信息的充分共享。网格系统强大的计算能力是 通过网格上任务的运行性能体现的，在网格环境中，任务调度问题是 网格研究领域的关键问题之一。由于网格环境的动态性、分布性及异 构性等特点，网格任务调度面临巨大的挑战。本文在分析网格任务调 度的过程、目标和特点的基础上，着重做了如下工作：1、在介绍遗传 算法(GeneticAlgorithm, GA)实现过程和算法优缺点的基础上，提出 了改进的粗粒度并行遗传算法。该算法在交叉阶段设计了一种基于模 式阶的多点交叉算子，在

3、变异阶段采用一种基于任务迁移的定向变异购买全文在线加 QQ 13993 8848 即付即发同时提供论文写作指导和学术期刊发表服务在:本立为学术立献总库合作提供I如有异溟请与总库或学校联系0方法，同时采用精英策略保持种群的多样性。此外，还引入统计学中“移动平均”的原理来预测种群进化若干代后适应度值的变化趋势。2、在介绍免疫进化算法(Immune Evolutionary Algorithm, IEA) 实 现过程和算法优缺点的基础上，提出了自适应的免疫进化算法。将免 疫机制引入到适应度函数的设计中，且将调节因子 k 调整为自适应变 化的因子。实验结果表明改进的算法能够改善免疫进化算法的不足，很好

4、地保持种群的多样性。3、利用 GridSim 仿真器对改进的算法进 行了多次的仿真实验，对比分析了改进前后算法的性能。实验结果表 明,与改进前算法的性能相比较，改进后的算法有更快的收敛速度和 更佳的寻优能力，并且能够获得更优的调度结果。【英文摘要】 Gird system is curre ntly most widely used distributedapplicati on system. It makes use of Internet to connect varieties ofresources with geographical distribution into a virt

5、ual organism which issimilar to a logical whole ora super computer, and the users can not only enjoy integrative informationand application services, but also share resources and do collaborativework. Thearesource island ” phenomenon doesn t exist in Grid and itaccomplishes full sharing of in format

6、io n.The comput ing capability of GridSystem is reflected by the running performance of grid tasks, in gridenvironment, task scheduling is one of the pivotal problems inthe grid research field. However, due to dynamic, distributen购买全文在线加 QQ 13993 8848 即付即发同时提供论文写作指导和学术期刊发表服务在:本立为学术立献总库合作提供I如有异溟请与总库或

7、学校联系0and heteroge neous characters of grid en vir onment, task scheduli ng isfacing a treme ndous challe nge. On the basis of an alyz ing the process,targets and characteristic of task scheduli ng, this paper emphatically hasdone the follow ing works:1. An improved coarse-grained parallel geneticalg

8、orithm is proposed on the basis of introducing implementationprocess,adva ntages and disadva ntages of gen etic algorithm. The algorithmdesig ns a multi-po int crossover operator based on schema order in thecrossover phase and adopts a vectored mutati on method based on taskmigratio n in mutati on p

9、hase. Whaf s more, it uses elitist strategy to keepthe population diversity .In additi on, the theory of movi ng average instatistics is also in troduced into the improved algorithm to predict the changing tendency of the populati on fitn ess after several generations in theevolution.2. An improved immune algorithm is proposed on the basis of introduc ing implementation process, advantages and disadvantages ofimmune evoluti onary algorithm. The immune m