基于网格资源超图模型的可信任务调度

网格任务调度是当前重要的研究领域。网格环境具有动态性、异构性等特点,网格资源的处理性能和稳定性都是影响到任务调度顺利完成的重要因素。为了获得更小的任务完成时间,该文根据网格环境的特点,建立了网格资源超图模型,在该模型基础上对资源按性能进行聚类,并提出一种可信任务调度算法GRHTS。模拟实验结果表明,该基于网格资源超图模型的可信任务调度算法优于同类算法,是一种有效的网格任务调度算法。     

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.     

GCSS: a global collaborative scheduling strategy for wide-area high-performance computing

Wide-area high-performance computing is widely used for large-scale parallel computing applications owing to its high computing and storage resources. However, the geographical distribution of computing and storage resources makes efficient task distribution and data placement more challenging. To achieve a higher system performance, this study proposes a two-level global collaborative scheduling strategy for wide-area high-performance computing environments. The collaborative scheduling strategy integrates lightweight solution selection, redundant data placement and task stealing mechanisms, optimizing task distribution and data placement to achieve efficient computing in wide-area environments. The experimental results indicate that compared with the state-of-the-art collaborative scheduling algorithm HPS+, the proposed scheduling strategy reduces the makespan by 23.24%, improves computing and storage resource utilization by 8.28% and 21.73% respectively, and achieves similar global data migration costs.     

云计算中基于优先级和费用约束的任务调度算法

针对云计算中的服务质量保证问题,提出一种基于优先级和费用约束的任务调度算法。该算法通过计算任务优先级和资源服务能力,分别对任务和资源进行排序和分组,并根据优先级高低和服务能力强弱建立任务组和资源组间的调度约束关联;再通过计算任务在关联资源组内不同资源上的完成时间和费用,将任务按优先级高低依次调度到具有任务完成时间和费用折中值最小的资源上。与Min-Min和QoS-Guided-Min算法的对比实验结果表明,该算法具有良好的系统性能和负载均衡性,并降低了服务总费用。     

一种基于人工免疫的新的网格资源调度算法

由于广域网性能的巨大提高和功能强大且价格低廉的计算机不断增多,网格计算以一种极具有前途和吸引力的新范式出现。网格计算是集成地理位置分布,异构,多领域资源的一种平台,它提供透明、安全、同等、高性能资源共享。要获取计算网格中潜在的能量,设计一种有效和高效的网格资源调度算法很重要。网格独特的特点使得网格环境下的资源调度是相当复杂的。本文将重点设计一种新的基于免疫算法的网格资源调度算法。     

基于匹配规则的MapReduce任务调度模型

基于开源云计算平台Hadoop的MapReduce是当前流行的分布式计算框架之一,然而其先进先出(FIFO)调度算法存在资源利用效率低下的问题。提出了一种基于资源匹配规则的MapReduce任务调度模型并进行了算法实现。该调度模型通过获取任务的资源需求与计算节点的剩余资源,依据资源的匹配性进行任务分配,提高了系统的资源使用效率。首先对MapReduce的调度过程进行建模,提出了资源及匹配度的量化定义和相应的计算公式;然后给出了资源测量的具体方法及算法实现;最后利用TeraSort、GrepCount和WordCount任务与FIFO调度算法进行实验对比,实验结果显示,最好的情况下,提出的调度模型任务完成时间减少了22.19%,而最差情况下的吞吐量也提高了25.39%。     

光网络环境中计算任务与光路的联合调度算法

针对光网络环境下分布式计算系统的资源调度问题,提出了一种光网络计算任务和光路联合调度方案。该方案将光网络的特性加入到传统调度模型中,提出了计算任务与光路通信的联合调度模型,设计求解联合调度模型的扩展型列表算法。仿真实验验证了联合调度的有效性。     

云平台下基于截止时间的自适应调度策略

针对在共享集群中进行任务调度时,无法兼顾任务的响应速度与任务完成时间的问题,提出一种基于截止时间的自适应调度算法。该算法以用户提交的截止时间为依据,根据任务的执行进度自适应地分配适当的计算资源。不同于传统调度方式里由用户提交固定资源参数,该算法在资源约束的情况下会对优先级高的任务进行抢占式调度以保证服务质量（QoS）,并在抢占过程结束后额外分配资源补偿被抢占的任务。在Spark平台进行的任务调度实验结果显示,与另一种资源协调者（YARN）框架下的调度算法相比,所提算法能严格地控制短任务的响应速度,并使长作业的任务完成时间缩短35%。     

分布式计算中基于资源分级的自适应Min-Min算法*

Min-Min任务调度算法的思路总是优先调度执行时间较短的小任务,无法得到理想的最优跨度及资源负载平衡．针对该问题,提出基于资源分级的自适应Min-Min算法．分配任务前,先参考现有资源的属性进行分级处理,再与任务在资源中的最小完成时间作乘积得到的最小任务资源组合进行调度;在任务调度过程中,引入自适应阈值,调节长任务的调度等级,从而达到优化效果．通过模拟仿真实验,表明该算法在时间跨度和负载平衡上均有较好性能．     

网格资源超图划分聚类任务调度

网格计算是当前一个活跃的研究领域,其中任务调度是实现网格计算目标的一个重要部分.为获得良好的网格任务调度性能,提出了一种基于资源超图划分聚类的网格任务调度算法RHPC.该算法根据网格环境下资源数量庞大、异构、多样的特点,在构建的网格资源超图模型基础上,预先对资源进行性能划分聚类,将任务与聚类资源相匹配并实施调度.模拟实验结果证明算法缩短了任务资源相匹配的时间,提高了任务调度的性能,是一种有效的网格任务调度算法.     

一种异构环境下的基于MapReduce任务调度改进机制

针对在异构环境下采用现有MapReduce任务调度机制可能出现各计算节点间数据迁移和系统资源分配难以管理的问题, 提出一种动态的任务调度机制来改善这些问题。该机制先根据节点的计算能力按比例放置数据, 然后通过资源预测方法估计异构环境下MapReduce任务的完成时间, 并根据完成时间计算任务所需的资源。实验结果表明, 该机制提高了异构环境下任务的数据本地性比例, 且能动态地调整资源分配, 以保证任务在规定时间内完成, 是一种有效可行的任务调度机制。     

网格优化有向超图任务调度算法

任务调度是网格计算的一个重要部分.分析网格环境下任务调度的特点以及传统DAG图的优缺点,吸取有向超图的优点,将有向超图理论融合网格环境特征,建立了网格环境下的优化有向超图模型,并在此基础上通过网格优化有向超图的水平构形、标号及带宽计算实现任务对网格资源的映射与调度,提出网格优化有向超图任务调度算法GODHTS.模拟实验结果证明了该模型及其算法的有效性和优越性.     

Reputation-based dependable scheduling of workflow applications in Peer-to-Peer Grids

Grids facilitate creation of wide-area collaborative environment for sharing computing or storage resources and various applications. Inter-connecting distributed Grid sites through peer-to-peer routing and information dissemination structure (also known as Peer-to-Peer Grids) is essential to avoid the problems of scheduling efficiency bottleneck and single point of failure in the centralized or hierarchical scheduling approaches. On the other hand, uncertainty and unreliability are facts in distributed infrastructures such as Peer-to-Peer Grids, which are triggered by multiple factors including scale, dynamism, failures, and incomplete global knowledge.In this paper, a reputation-based Grid workflow scheduling technique is proposed to counter the effect of inherent unreliability and temporal characteristics of computing resources in large scale, decentralized Peer-to-Peer Grid environments. The proposed approach builds upon structured peer-to-peer indexing and networking techniques to create a scalable wide-area overlay of Grid sites for supporting dependable scheduling of applications. The scheduling algorithm considers reliability of a Grid resource as a statistical property, which is globally computed in the decentralized Grid overlay based on dynamic feedbacks or reputation scores assigned by individual service consumers mediated via Grid resource brokers. The proposed algorithm dynamically adapts to changing resource conditions and offers significant performance gains as compared to traditional approaches in the event of unsuccessful job execution or resource failure. The results evaluated through an extensive trace driven simulation show that our scheduling technique can reduce the makespan up to 50% and successfully isolate the failure-prone resources from the system.     

一种基于综合匹配度的边缘计算系统任务调度方法

边缘计算模式满足数据的实时和低功耗处理需求,是缓解当前网络数据洪流实时处理问题的有效方法之一.但边缘设备资源的异构与多样性给任务的调度与迁移带来极大的困难与挑战.目前,边缘计算任务调度研究主要集中在调度算法的设计与仿真,这些算法和模型通常忽略了边缘设备的异构性和边缘任务的多样性,不能使多样化的边缘任务与异构的资源能力深...     

SLA-aware energy-efficient scheduling scheme for Hadoop YARN

Apache Hadoop becomes ubiquitous for cloud computing which provides resources as services for multi-tenant applications. YARN (a.k.a. MapReduce 2.0) is one of the key features in the second-generation Hadoop, which provides resource management and scheduling for large-scale MapReduce environments. Two enormous challenges in the YARN scheduler are the abilities to automatically tailor and control resource allocations to different jobs for achieving their Service Level Agreements (SLAs), and minimize energy consumption of the overall cloud computing system. In this work, we propose an SLA-aware energy-efficient scheduling scheme which allocates appropriate amount of resources to MapReduce applications with YARN architecture. In our task scheduling policy, We consider the data locality information to save the MapReduce network traffic. Furthermore, the slack time between the actual execution time of completed tasks and expected completion time of the application is utilized to improve the energy-efficiency of the system. An online userspace governor-based dynamic voltage and frequency scaling (DVFS) scheme is designed in the YARN per-application ApplicationMaster to dynamically change the CPU frequency for upcoming tasks given the slack time from previous completed tasks. Experimental evaluation shows that our proposed scheme outperforms the existing MapReduce scheduling policies in terms of both resource ultization and energy-efficiency.     

Characterizing application scheduling on edge,fog, and cloud computing resources

Cloud computing has grown to become a popular distributed computing service offered by commercial providers. More recently, edge and fog computing resources have emerged on the wide-area network as part of Internet of things (IoT) deployments. These three resource abstraction layers are complementary, and offer distinctive benefits. Scheduling applications on clouds has been an active area of research, with workflow and data flow models offering a flexible abstraction to specify applications for execution. However, the application programming and scheduling models for edge and fog are still maturing, and can benefit from learnings on cloud resources. At the same time, there is also value in using these resources cohesively for application execution. In this article, we offer a taxonomy of concepts essential for specifying and solving the problem of scheduling applications on edge, fog, and cloud computing resources. We first characterize the resource capabilities and limitations of these infrastructure and offer a taxonomy of application models, quality-of-service constraints and goals, and scheduling techniques, based on a literature review. We also tabulate key research prototypes and papers using this taxonomy. This survey benefits developers and researchers on these distributed resources in designing and categorizing their applications, selecting the relevant computing abstraction(s), and developing or selecting the appropriate scheduling algorithm. It also highlights gaps in literature where open problems remain.     

物联网环境下具有顺序约束关系的静态任务表调度算法

针对物联网异构调度环境下并行计算的静态任务调度问题,提出了一种基于最早完成时间策略改变调度顺序的表调度算法HDPTS。该算法针对现有表调度算法在调度前不能准确地确定调度顺序的问题,在IHEFT算法的基础上添加了一个动态优先级调度策略,当节点的前驱任务都已经完成调度任务时,就改变该节点的调度优先级。任务优先级的计算在所有前驱任务到达这个任务的最晚完成时间与所有资源上最大可以使用时间之间取最大值的基础上,同时考虑到分配到各个资源上的任务对后继任务的影响和资源上的负载情况,以及上行权重的计算值和对出口任务的影响,使得优先级计算更加合理,能够根据任务分配动态合理改变任务调度顺序。通过随机生成一个算例进行测试,结果表明HDPTS比IHEFT、HEFT在调度长度方面减少14.29%;对大量随机产生的特定结构的有向无环图(DAG)进行测试,测试结果显示HDPTS算法比IHEFT、HEFT和LDCP算法更有效。     

信任约束下的网格工作流任务调度算法*

提出了信任约束下的网格工作流任务调度算法。该算法结合直接经验和推荐经验计算资源的信任度,根据任务在候选资源上的执行时间确定关键任务,然后选择满足执行时间和信任综合函数的资源。实验结果表明。该算法不仅缩短了工作流的完成时间,而且提高了调度的成功率。     

网格计算中基于并行克隆遗传算法的任务分配与调度

实现网格计算的一个重要目的在于实现地理分布、异构资源的统一描述方法,提供用户虚拟的统一资源界面,并将用户提出的服务要求透明、动态地分配给最适应的资源上执行。针对目前任务调度的应用现状,提出了一种既能使资源负载均衡又能充分利用系统资源的并行克隆遗传算法,该启发式算法能显著地降低资源最优分配中的计算复杂度,使其能满足实时调度的需要。实验结果表明这种算法优于其他调度算法。     

异构云环境下基于分簇的云资源感知任务调度方案

针对提高异构云平台中资源调度的效率,提出了一种基于任务和资源分簇的异构云计算平台任务调度方案。利用K-means算法,根据任务的CPU和I/O处理时间对任务分簇,根据资源的计算能力对资源分簇;然后,将任务簇对应到合适的资源簇,并利用最早截止时间优先（EDF）算法对任务簇中的独立任务进行调度,利用提出的改进型最小关键路径（MCP）算法对依赖性任务进行调度。实验结果表明,在资源异构的云计算环境中,该方案执行任务时间短、能耗低。