多机相关任务的均衡调度算法

多机相关任务的均衡调度算法许日滨（青岛大学计算机科学系青岛２６６０７１）ＴＨＥＥＱＵＩＬＩＢＲＩＵＭＳＣＨＥＤＵＬＩＮＧＡＬＧＯＲＩＴＨＭＦＯＲＤＥＰＥＮＤＥＮＴＴＡＳＫＳＩＮＭＵＬＴＩＰＲＯＣＥＳＳＯＲＳ￥ＸｕＹｕｅｂｉｎ（ＤｅｐａｒｔｍｅａｔＣｍ...     

求多总线系统容错度的多项式时间算法

求多总线系统容错度的多项式时间算法陈四清，周六丁（重庆大学计算机科学系重庆６３００４４）ＰＯＬＹＮＯＭＩＡＬ－ＴＩＭＥＡＬＧＯＲＩＴＨＭＳＦＯＲＤＥＴＥＲＭＩＮＡＴＩＮＧＴＨＥＦＡＵＬＴ－ＴＯＬＥＲＡＮＣＥＤＥＧＲＥＥＯＦＭＵＬＴＩＢＵＳＳＹＳＴＥＭ...     

模拟退火算法与遗传算法的结合

模拟退火算法与遗传算法的结合王雪梅，王义和（哈尔滨工业大学计算机科学与工程系哈尔滨１５０００１）ＴＨＥＣＯＭＢＩＮＡＴＩＯＮＯＦＳＩＭＵＬＡＴＥＤＡＮＮＥＡＬＩＮＧＡＮＤＧＥＮＥＴＩＣＡＬＧＯＲＩＴＨＭＳ￥ＷＡＮＧＸｕｅｍｅｉ；ＷＡＮＧＹｉｈｅ（Ｄｅ...     

基于重叠三角形区域的运动估值算法

基于重叠三角形区域的运动估值算法薛向阳，吴立德（复旦大学计算机科学系上海２００４３３）ＭＯＴＩＯＮＥＳＴＩＭＡＴＩＯＮＡＬＧＯＲＩＴＨＭＵＳＩＮＧＯＶＥＲＬＡＰＰＥＤＴＲＩＡＮＧＵＬＡＲＰＡＴＣＨ￥ＸＵＥＸｉａｎｇｙａｎｇ；ＷＵＬｉｄｅ（Ｄｅｐａｒｔ...     

曲面间高阶几何连续拼接算法研究

曲面间高阶几何连续拼接算法研究卢小林，马利庄，何志均（浙江大学ＣＡＤ＆ＣＧ国家重点实验室杭州３１００２７）ＡＮＡＬＧＯＲＩＴＨＭＦＯＲＨＩＧＨＯＲＤＥＲＧＥＯＭＥＴＲＩＣＣＯＮＮＥＣＴＩＯＮＢＥＴＷＥＥＮＡＤＪＡＣＥＮＴＰＡＴＣＨＥＳ￥ＬｕＸｉａｏｌ...     

基于有向无环图的成本-时间优化调度算法

网格计算环境下,基于有向无环图（DAG）的成本-时间优化调度算法运用经济规律把网格用户的任务映射到网格资源中运行。OGS算法考虑了任务间的优先关系,使得任务完成时间最小,但没考虑到在网格环境中所需的成本。Nimrod/G模型中提出基于时间和成本限制下的优化调度算法（DBC）考虑了时间和成本,但没考虑任务间的优先关系。本文综合考虑了成本-时间因素以及任务间的优先关系,在不增加完成时间的基础上,把任务映射到价格便宜的机器上,提出了基于有向无环图的成本-时间优化调度算法。通过仿真表明,相对OGS算法,该算法减少了所需成本。     

用任意一个基求可行基与基本可行解的算法

用任意一个基求可行基与基本可行解的算法陈开周，郭强（西安电子科技大学）ＡＮＡＬＧＯＲＩＴＨＭＯＦＦＩＮＤＩＮＧＡＦＥＡＳＩＢＬＥＢＡＳＩＳＡＮＤＡＢＡＳＩＣＦＥＡＳＩＢＬＥＳＯＬＵＴＩＯＮＷｆＴＨＡＮＹＢＡＳＩＳ￥ＣｈｅｎＫａｉｚｈｏｕ；ＧｕｏＱｉａ...     

多机相关任务的相关矩阵调度算法

本文讨论了多机相关任务的调度问题，从时间和空间两方面考虑，提出了一种新的多机相关任务的调度算法－多机相关任务的相关矩阵调度算法（ＲＭＳＡ）。利用可变的相关矩阵Ｍｕ，表示任务的空间需求与处理机的局部存储空间的关系以及任务分配的状态。实验表明此算法具有较短的调度长度，并且具有较好的时间均衡性和空间协调性。     

一个组合优化问题的Threshold算法

一个组合优化问题的Ｔｈｒｅｓｈｏｌｄ算法罗宗俊（贵州民族学院）ＡＴＨＲＥＳＨＯＬＤＡＬＧＯＲＩＴＨＭＦＯＲＡＳＰＥＣＩＡＬＣＯＭＢＩＮＡＴＯＲＩＡＬＯＰＴＩＭＩＺＡＴＩＯＮＰＲＯＢＬＥＭ￥ＬｕｏＺｏｎｇｊｕｎ（ＧｕｉｚｈｏｕＮａｔｉｏｎａｌＭｉｎｏｒ...     

基于有向无环图的成本-时间优化调度算法

网格计算环境下,基于有向无环图(DAG)的成本-时间优化调度算法运用经济规律把网格用户的任务映射到网格资源中运行.OGS算法考虑了任务间的优先关系,使得任务完成时间最小,但没考虑到在网格环境中所需的成本.Nimrod/G模型中提出基于时间和成本限制下的优化调度算法(DBC)考虑了时间和成本,但没考虑任务问的优先关系.本文综合考虑了成本-时间因素以及任务间的优先关系,在不增加完成时间的基础上,把任务映射到价格便宜的机器上,提出了基于有向无环图的成本-时间优化调度算法.通过仿真表明,相对OGS算法,该算法减少了所需成本.     

一种基于负载均衡性的网格任务调度算法

针对二分图匹配算法在任务之间存在时序关系时无法进行有效调度以及EFT算法没有充分考虑各处理机性能及网络通信状况的问题,提出基于二分图匹配的改进ETF算法。该算法综合考虑任务之间的时序关系、处理机的性能、处理机之间的通信情况及已处理任务的调度情况,利用二分图最佳匹配思想对局部任务进行调度。实验表明该算法具有较小的调度长度和较好的负载均衡性。     

Honey bee behavior inspired load balancing of tasks in cloud computing environments

Scheduling of tasks in cloud computing is an NP-hard optimization problem. Load balancing of non-preemptive independent tasks on virtual machines (VMs) is an important aspect of task scheduling in clouds. Whenever certain VMs are overloaded and remaining VMs are under loaded with tasks for processing, the load has to be balanced to achieve optimal machine utilization. In this paper, we propose an algorithm named honey bee behavior inspired load balancing (HBB-LB), which aims to achieve well balanced load across virtual machines for maximizing the throughput. The proposed algorithm also balances the priorities of tasks on the machines in such a way that the amount of waiting time of the tasks in the queue is minimal. We have compared the proposed algorithm with existing load balancing and scheduling algorithms. The experimental results show that the algorithm is effective when compared with existing algorithms. Our approach illustrates that there is a significant improvement in average execution time and reduction in waiting time of tasks on queue.     

基于架构负载感知的虚拟机聚簇部署算法

针对云计算中虚拟机部署问题,提出了一种基于架构负载感知的虚拟机聚簇部署算法。首先计算云数据中心各层架构的负载,并在架构内对主机进行聚簇。虚拟机进行部署时,先按照相应的规则进行虚拟机间的聚簇,并优先选择负载较低的架构进行部署,架构选择后,进行虚拟机簇与主机簇的匹配以选择最优的主机簇进行部署。最后通过CloudSim进行仿真验证,将其与贪婪算法及基于架构负载感知的基本部署算法进行比较,证明了算法在部署时间方面有明显的优越性,并提高了网络资源的利用率。     

面向依赖关系约束的移动群智感知任务协作

现有移动群智感知中,大多研究将每个任务作为独立个体进行处理,对任务间约束关系缺乏研究,为此,提出了基于感知质量优先级的在线任务协作方法（online task collaboration method based on sensing quality priority,TCSP）。该方法首先使用贪婪算法计算感知质量优先级,对全部任务进行筛选以保证任务完成率;然后将选出任务中存在时间先后或执行逻辑前后关系的多个子任务构建为任务协作图,并将其协作过程建模为有约束的马尔可夫决策过程,通过强化学习算法求出最优协作策略。实验结果表明,与现有基线方法相比,所提出的任务协作方法能够减少依赖任务的平均完成时间,有效降低平台的平均感知成本。     

云计算环境下基于蜜蜂觅食行为的任务负载均衡算法

针对云计算环境下的任务调度程序通常需要较多响应时间和通信成本的问题,提出了一种基于蜜蜂行为的负载均衡(HBB-LB)算法。首先,利用虚拟机(VM)进行负载平衡来最大化吞吐量;然后,对机器上任务的优先级进行平衡;最后,将平衡重点放在减少VM等待序列中任务的等待时间上,从而提高处理过程的整体吞吐量和优先级。利用CloudSim工具模拟云计算环境进行仿真实验,结果表明,相比粒子群优化(PSO)、蚁群算法(ACO)、动态负载均衡(DLB)、先入先出(FIFO)和加权轮询(WRR)算法, HBB-LB算法的平均响应时间分别节省了5%、13%、17%、67%、37%,最大完成时间分别节省了20%、23%、18%、55%、46%,可以更好地平衡非抢占式独立任务,适用于异构云计算系统。     

异构系统中的综合性启发式任务调度算法

任务的单个属性常作为基于优先驱动的表调度算法的优先级,针对这种方法常出现优先级相同的情况,提出一个综合性启发式算法HCPFS。算法分三个优先级选择任务进行调度,从高到低依次为：关键路径上的任务、就绪任务到出口任务的路径长度和后继任务数。调度过程中,算法采用任务复制和空闲时间区段任务插入的方法。采用随机生成图法和任务图集进行了算法模拟和比较,实验数据表明HCPFS算法具有更好的调度性能。     

LATOC: an enhanced load balancing algorithm based on hybrid AHP-TOPSIS and OPSO algorithms in cloud computing

Providing required level of service quality in cloud computing is one of the most significant cloud computing challenges because of software and hardware complexities, different features of tasks and computing resources and also, lack of appropriate distribution of tasks in cloud computing environments. The recent research in this field show that lack of smart prioritization and ordering of tasks in scheduling (as an NP-hard problem) has been very effective and resulted in lack of load balancing, response time increase, total execution time increase and also, average resource use decrease. In line with this, the proposed method of this research called LATOC considered first the key criteria of an input task like required processing unit, data length of task and execution time. Then, it addressed task prioritization in separate queues using the technique for order preference by similarity to ideal solution (TOPSIS) and analytic hierarchy process (AHP) in figure of a hybrid intelligent algorithm (AHP-TOPSIS). Each ordered task in separate priority queues was placed based on its priority level, and then, to assign each task from each priority queue to virtual machines, optimized particle swarm optimization was used. Many simulations based on various scenarios in Cloudsim simulator show that smart assignment of prioritized tasks by LATOC resulted in improvement of important cloud computing parameters such as total execution time and average resource use comparing similar methods.

     

Static resource allocation for heterogeneous computing environments with tasks having dependencies,priorities, deadlines,and multiple versions

Heterogeneous computing (HC) environments composed of interconnected machines with varied computational capabilities are well suited to meet the computational demands of large, diverse groups of tasks. One aspect of resource allocation in HC environments is matching tasks with machines and scheduling task execution on the assigned machines. We will refer to this matching and scheduling process as mapping. The problem of mapping these tasks onto the machines of a distributed HC environment has been shown, in general, to be NP-complete. Therefore, the development of heuristic techniques to find near-optimal solutions is required. In the HC environment investigated, tasks have deadlines, priorities, multiple versions, and may be composed of communicating subtasks. The best static (off-line) techniques from some previous studies are adapted and applied to this mapping problem: a genetic algorithm (GA), a GENITOR-style algorithm, and a two phase greedy technique based on the concept of Min–min heuristics. Simulation studies compare the performance of these heuristics in several overloaded scenarios, i.e., not all tasks can be executed by their deadlines. The performance measure used is the sum of weighted priorities of tasks that completed before their deadline, adjusted based on the version of the task used. It is shown that for the cases studied here, the GENITOR technique finds the best results, but the faster two phase greedy approach also performs very well.