A high performance algorithm for static task scheduling in heterogeneous distributed computing systems

Effective task scheduling is essential for obtaining high performance in heterogeneous distributed computing systems (HeDCSs). However, finding an effective task schedule in HeDCSs requires the consideration of both the heterogeneity of processors and high interprocessor communication overhead, which results from non-trivial data movement between tasks scheduled on different processors. In this paper, we present a new high-performance scheduling algorithm, called the longest dynamic critical path (LDCP) algorithm, for HeDCSs with a bounded number of processors. The LDCP algorithm is a list-based scheduling algorithm that uses a new attribute to efficiently select tasks for scheduling in HeDCSs. The efficient selection of tasks enables the LDCP algorithm to generate high-quality task schedules in a heterogeneous computing environment. The performance of the LDCP algorithm is compared to two of the best existing scheduling algorithms for HeDCSs: the HEFT and DLS algorithms. The comparison study shows that the LDCP algorithm outperforms the HEFT and DLS algorithms in terms of schedule length and speedup. Moreover, the improvement in performance obtained by the LDCP algorithm over the HEFT and DLS algorithms increases as the inter-task communication cost increases. Therefore, the LDCP algorithm provides a practical solution for scheduling parallel applications with high communication costs in HeDCSs.     

基于多目标免疫系统算法的云任务调度策略

针对云计算环境下任务调度问题,为减少任务完工时间,同时降低任务执行费用,提出一种改进的基于多目标免疫系统的任务调度算法IMISA来寻找较优的可行分配方案。与传统分配适应度值不同,该算法将抗体群划分为非支配解集和支配解集,分别将非支配解的独立支配区域面积、支配解与所有非支配解所围成的多边形面积作为相应的抗体-抗原亲和力,根据相应亲和度计算克隆比例后克隆变异生成子代。在CloudSim平台上进行仿真实验,结果表明,与NSGA-Ⅱ及多目标免疫系统算法(MISA)相比,IMISA能够找到具有更短完工时间及更小的执行费用的调度方案,同时获得的Pareto解集也具有更好的分布性。     

基于改进粒子群算法的云计算任务调度算法

如何对任务进行高效合理的调度是云计算需要解决的关键问题之一,针对云计算的编程模型框架,在传统粒子群优化算法（PSO）的基础上,提出了一种具有双适应度的粒子群算法（DFPSO）。通过该算法不但能找到任务总完成时间较短的调度结果,而且此调度结果的任务平均完成时间也较短。仿真分析结果表明,在相同的条件设置下,该算法优于传统的粒子群优化算法,当任务数量增多时,其综合调度性能优点明显。     

云计算环境下基于路径优先级的任务调度算法

为了最小化云计算系统的任务调度长度,结合表启发式调度技术和任务复制的思想提出基于路径优先权的任务调度算法.采用一种新方法计算DAG图中任务节点及边的权值,从最高优先权的路径开始依次选择任务进行调度,并通过有选择性地复制任务节点的父任务来减少任务间信息传送的时间花费,最后将任务安排到使其执行完成时间最早的虚拟机上.通过随机产生的DAG图与HEFT算法进行对比分析,实验结果表明了该算法能获得较短的调度长度.     

A low-power task scheduling algorithm for heterogeneous cloud computing

The Journal of Supercomputing - As a new type of computing, cloud computing has led to a major computational change. Among many technologies in cloud computing, task scheduling has always been...     

云计算环境下基于可靠性感知的任务调度算法

针对云计算环境下并行任务易受资源失效的影响而无法完成,且动态提供云资源可靠性较低的问题,首先,引入失效恢复机制,由于在失效可恢复情况下资源失效规律动态变化,使用两参数Weibull分布对不同时段资源节点和通信链路失效规律的局部特征进行描述;然后,根据并行任务之间存在的各类交互关系分析,提出了一种基于变参数失效规则的资源可靠性评估模型;最后,将该模型并入粒子群算法得到基于可靠性感知的自适应惯性权重粒子群资源调度算法R PSO,从而在计算适应度时充分考虑备选资源的可靠程度。仿真实验结果表明,当选择了合适的失效恢复参数时,提出的R PSO算法能够大幅度提高云服务可靠性,且只会增加少量的额外失效恢复开销。     

基于改进GA的云计算任务调度算法

云计算通常需要处理大量的计算任务,任务调度策略在决定云计算效率方面起着关键作用。如何合理地分配计算资源,有效地调度任务运行,使所有任务运行完成所需的时间较短、成本较小是个重要的问题。提出一种考虑时间-成本约束的遗传算法(TCGA),通过此算法调度产生的结果不仅能使任务完成所需的时间较短,而且成本较小。通过实验,将TCGA与考虑时间约束的遗传算法(TGA)、考虑成本约束的遗传算法(CGA)进行比较,实验结果表明,该算法是云计算中一种有效的任务调度算法。     

基于IFWA-ABC的云计算资源任务调度算法的研究

针对云计算资源任务调度效率低,资源分配不均的情况,将改进的烟花算法和人工蜂群算法算法进行融合为IFWA-ABC。首先,对云计算资源任务调度进行描述;其次,在FWA初始化中采用混沌反向学习和柯西分布进行优化,对核心烟花和非核心烟花的半径分别进行优化,将FWA中最优个体通过改进的ABC算法进行获得;最后,将IFWA-ABC算法用于云计算任务调度。仿真实验中,通过与FWA、ABC在虚拟机、执行时间、消耗成本、能量消耗指标对比中,IFWA-ABC具有明显的优势能够有效地提高云计算资源分配效率。     

不确定性感知的边缘计算任务调度算法

任务执行时长的不确定性是设计任务调度算法时的一个重要问题,关系到调度方案能否满足任务的截止时间要求.鉴于此,研究不确定性感知的边缘计算任务调度问题,以最小化边缘提供商开销为优化目标建立任务调度问题的优化模型.该模型将任务执行时长建模为随机变量并推导出任务完成时间的完整概率分布,引入关于任务截止时间的概率约束,以可调节的概率阈值保证任务按时完成.为求解该问题,进一步提出基于蝙蝠算法搜索策略的元启发式算法,包含两个关键的算法组件,映射算子实现蝙蝠空间与调度解空间的关联,评估算子实现候选解可行性的判定和优化目标值的计算.基于对比实验的仿真结果表明,所提出算法能够得到高质量的任务调度方案.     

一种基于遗传—蚁群算法的网格任务调度策略*

针对遗传调度算法局部求解能力不足、容易早熟和退化的问题,以及蚁群调度算法初始搜索阶段效率低下的缺陷,充分应用遗传算法全局搜索能力较好和蚁群算法求解精度较高的优势,提出了一种基于遗传-蚁群算法的网格任务调度策略.该方法集成了遗传算法和蚁群算法的双重优点.仿真测试结果表明,提出的网格任务调度方法总体上优于遗传算法和蚁群算法...     

基于贪心改进算法的云计算任务调度

基于云计算的存储和计算架构的特征上,对资源存储算法和任务分配进行了研究.针对云计算的资源管理中单纯考虑算法的时间和空间复杂度,而忽略在数据链路层因调度所消耗的时间问题,因此将网络存储感知和贪心算法相结合,提出了一种贪心改进算法,目的在于大幅减少数据在数据链路层所消耗的时间.最终在CloudSim平台上进行云环境下的仿真,将得出的结果和一般的贪心算法相比较,经过对比分析表明:改进后的贪心算法对于任务的执行而言时间更短,效率更高.     

A novel multiclass priority algorithm for task scheduling in cloud computing

The Journal of Supercomputing - Task scheduling is an attractive research topic in cloud computing nowadays. This process is very challenging and well known as NP-complete problem. Due to the...     

一种云环境下基于混合型BBO的任务调度算法

任务调度在云计算中占有重要地位,是影响云计算性能的关键因素,被证明是NP问题。启发式算法是解决该问题的最有效方法之一,针对近年来出现的一种新型启发式算法--BBO算法展开研究,由于BBO算法在求解过程中收敛速度较慢,因此结合粒子群算法提出了一种新型算法的任务调度算法--HMBBO,并结合Cloudsim云仿真平台,进行了以Makespan为目标函数的比对实验。实验结果表明,与几种经典的启发式算法相比,HMBBO算法具有寻优能力强、收敛速度快、求解质量高的特点,为解决云计算环境中任务调度问题提供了一种新思路。     

Multi-objective hybrid genetic algorithm for task scheduling problem in cloud computing

Neural Computing and Applications - The cloud computing systems are sorts of shared collateral structure which has been in demand from its inception. In these systems, clients are able to access...     

云平台下动态任务调度人工免疫算法

针对云计算领域的任务调度问题,提出了一种基于人工免疫(AI)理论的云计算平台动态任务调度算法。该算法首先利用排队论迅速、粗略地确定云计算平台保持稳态的条件,并为后面的计算提供基础数据;然后利用人工免疫理论中的免疫克隆选择算法,搜索出为集群中各节点上的不同虚拟机分配计算资源的近似最优配置;算法中还加入了适当的负载平衡处理,它使抗体基因更加优良。模拟实验结果表明,该调度算法能有效提高收敛速度和精度,快速搜索到合理配置,提高了集群资源利用率。     

An efficient list-based task scheduling algorithm for heterogeneous distributed computing environment

The task scheduling in heterogeneous distributed computing systems plays a crucial role in reducing the makespan and maximizing resource utilization. The diverse nature of the devices in heterogeneous distributed computing systems intensifies the complexity of scheduling the tasks. To overcome this problem, a new list-based static task scheduling algorithm namely Deadline-Aware-Longest-Path-of-all-Predecessors (DA-LPP) is being proposed in this article. In the prioritization phase of the DA-LPP algorithm, the path length of the current task from all its predecessors at each level is computed and among them, the longest path length value is assigned as the rank of the task. This strategy emphasizes the tasks in the critical path. This well-optimized prioritization phase leads to an observable minimization in the makespan of the applications. In the processor selection phase, the DA-LPP algorithm implements the improved insertion-based policy which effectively utilizes the unoccupied leftover free time slots of the processors which improve resource utilization, further least computation cost allocation approach is followed to minimize the overall computation cost of the processors and parental prioritization policy is incorporated to further reduce the scheduling length. To demonstrate the robustness of the proposed algorithm, a synthetic graph generator is used in this experiment to generate a huge variety of graphs. Apart from the synthetic graphs, real-world application graphs like Montage, LIGO, Cybershake, and Epigenomic are also considered to grade the performance of the DA-LPP algorithm. Experimental results of the DA-LPP algorithm show improvement in performance in terms of scheduling length ratio, makespan reduction rate , and resource reduction rate when compared with other algorithms like DQWS, DUCO, DCO and EPRD. The results reveal that for 1000 task set with deadline equals to two times of the critical path, the scheduling length ratio of the DA-LPP algorithm is better than DQWS by 35%, DUCO by 23%, DCO by 26 %, and EPRD by 17%.     

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。     

SmartGridRPC: The new RPC model for high performance Grid computing

The paper presents the SmartGridRPC model, an extension of the GridRPC model, which aims to achieve higher performance. The traditional GridRPC provides a programming model and API for mapping individual tasks of an application in a distributed Grid environment, which is based on the client‐server model characterized by the star network topology. SmartGridRPC provides a programming model and API for mapping a group of tasks of an application in a distributed Grid environment, which is based on the fully connected network topology. The SmartGridRPC programming model and API and its performance advantages over the GridRPC model are outlined in this paper. In addition, experimental results using a real‐world application are also presented. Copyright © 2010 John Wiley & Sons, Ltd.     

云计算环境下基于模板的任务调度策略与算法

针对云计算任务调度,提出了一种基于模板的任务调度（Template-based Task Scheduling,TTS）策略。该策略充分考虑了通信开销,在对任务分配进行预处理的基础上实现任务调度,主要分为两步：针对一个任务集合,采用可分任务调度求解子任务大小的方法,求出各个处理机应该分担的任务量模板;根据求出的模板,采用合理的调度算法对任务进行调度,从而得到较优的调度结果。在TTS策略下,对传统贪心算法加以改进,最终提出基于模板的任务调度贪心算法（Template-based Task Scheduling Greedy Algorithm,TTSGdA）。与Min-min算法和遗传算法的对比实验结果表明,TTSGdA能够有效减少任务集合完成时间。