DAG分割模型下的云工作流调度策略

为了优化云工作流调度的经济代价和执行效率,提出一种基于有向无循环图（DAG）分割的工作流调度算法PBWS。以工作流调度效率与代价同步优化为目标,算法将调度求解过程划分为三个阶段进行：工作流DAG结构分割、分割结构调整及资源分配。工作流DAG结构分割阶段在确保任务间执行顺序依赖的同时求解初始的任务分割图;分割结构调整阶段以降低执行跨度为目标,在不同分割间对任务进行重分配;资源分配阶段旨在选择代价最高效的任务与资源映射关系,确保资源的总空闲时间最小。利用五种科学工作流DAG模型对算法进行了仿真实验。结果表明。PBWS算法仅以较小的执行跨度为开销,极大降低了工作流执行代价,实现了调度效率与调度代价的同步优化,其综合性能是优于同类型算法的。     

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.     

Bi-level fuzzy based advanced reservation of Cloud workflow applications on distributed Grid resources

The increasing demand on execution of large-scale Cloud workflow applications which need a robust and elastic computing infrastructure usually lead to the use of high-performance Grid computing clusters. As the owners of Cloud applications expect to fulfill the requested Quality of Services (QoS) by the Grid environment, an adaptive scheduling mechanism is needed which enables to distribute a large number of related tasks with different computational and communication demands on multi-cluster Grid computing environments. Addressing the problem of scheduling large-scale Cloud workflow applications onto multi-cluster Grid environment regarding the QoS constraints declared by application’s owner is the main contribution of this paper. Heterogeneity of resource types (service type) is one of the most important issues which significantly affect workflow scheduling in Grid environment. On the other hand, a Cloud application workflow is usually consisting of different tasks with the need for different resource types to complete which we call it heterogeneity in workflow. The main idea which forms the soul of all the algorithms and techniques introduced in this paper is to match the heterogeneity in Cloud application’s workflow to the heterogeneity in Grid clusters. To obtain this objective a new bi-level advanced reservation strategy is introduced, which is based upon the idea of first performing global scheduling and then conducting local scheduling. Global-scheduling is responsible to dynamically partition the received DAG into multiple sub-workflows that is realized by two collaborating algorithms: (1) The Critical Path Extraction algorithm (CPE) which proposes a new dynamic task overall critically value strategy based on DAG’s specification and requested resource type QoS status to determine the criticality of each task; and (2) The DAG Partitioning algorithm (DAGP) which introduces a novel dynamic score-based approach to extract sub-workflows based on critical paths by using a new Fuzzy Qualitative Value Calculation System to evaluate the environment. Local-scheduling is responsible for scheduling tasks on suitable resources by utilizing a new Multi-Criteria Advance Reservation algorithm (MCAR) which simultaneously meets high reliability and QoS expectations for scheduling distributed Cloud-base applications. We used the simulation to evaluate the performance of the proposed mechanism in comparison with four well-known approaches. The results show that the proposed algorithm outperforms other approaches in different QoS related terms.     

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.     

基于QoS的网格资源管理

资源管理是网格计算研究领域中的一项重要研究点,目前的研究工作大多致力于解决异构性问题,对于在交付无缝QoS(quality of service)前提下提高资源分配性能方面还缺乏深入的研究.而且,目前对网格QoS的研究集中于将多媒体网络QoS的相关成果融入网格体系架构中以提供对网格QoS的支持,而缺少对网格QoS的特性进行系统化的研究与归纳.为此,提出了网格QoS的层次结构模型,并对其中承上启下的虚拟组织层QoS参数进行了新的分类和测量;然后,利用SNAP(service negotiation and acquisition protocol)协议对基于网格QoS层次结构模型的网格QoS参数的映射转换过程进行了分析;最后,设计了网格资源管理仿真系统,并运用相关的网格QoS的研究改进了现有的Min-Min算法.仿真实验结果表明,基于QoS的网格资源管理可以在满足用户QoS需求下,有效地提高网格资源的利用率和服务请求的成功率.     

改进型Dijkstra算法在最优路径选择中的应用研究

为了有效提高市民出行效率。减小城市资源消耗,优化城市环境,在改进型Dijkstra算法的基础上设计并实现城市公交线路查询系统。Dijkstra算法是图论中求解最短路径问题的一种优秀算法。在Dijkstra算法的基础上．进行对存储结构进行优化、对结点进行分类、对权值进行排序这三大改进．并将改进型Dijkstra算法作为最优公交线路查询算法的核心。以时间最小、费用最小、综合最优这三种需求作为评价标准分别建立数学模型,通过数学模型将搜索两点间的最优路线问题转化成求解两点间的最短路径问题．最后以南昌市的公交路网为例对算法和数学模型进行仿真测试。仿真结果验证算法和数据模型的有效性。     

云计算环境中面向DAG任务的多目标调度算法

为了实现任务执行效率与执行代价的同步优化,提出了一种云计算环境中的DAG任务多目标调度优化算法。算法将多目标最优化问题以满足Pareto最优的均衡最优解集合的形式进行建模,以启发式方式对模型进行求解;同时,为了衡量多目标均衡解的质量,设计了基于hypervolume方法的评估机制,从而可以得到相互冲突目标间的均衡调度解。通过配置云环境与三种人工合成工作流和两种现实科学工作流的仿真实验测试,结果表明,比较同类单目标算法和多目标启发式算法,算法不仅求解质量更高,而且解的均衡度更好,更加符合现实云的资源使用特征与工作流调度模式。     

基于拓扑序列归约的Web服务组合QoS度量算法

考虑有向无环图 (DAG)描述的组合服务模型,提出了一种新的组合服务QoS度量方法--基于拓扑序列归约的Web服务QoS度量方法（QCMTSR）。其借鉴迭代归约度量方法中的基本结构及QoS计算公式,定义了DAG图中的两类基本结构,串归约结构和并归约结构,并给出了两种基本结构的QoS属性计算公式;通过逐步归约DAG图拓扑序列中的每个节点,直至最后一个节点的QoS属性值就是组合服务的各QoS属性的度量结果。从理论上证明了QCMTSR算法适用于所有DAG描述的组合服务,并实验证明QCMTSR算法对可靠性和可用性能够更准确的度量。     

Scheduling DAG-based applications in multicluster environments with background workload using task duplication

Grids or multicluster computing environments are becoming increasingly popular to both scientific and commercial applications. Process scheduling remains a central issue to be effectively resolved in order to exploit the full potential that the grid or multicluster environment can offer. We use a directed acyclic graph (DAG) to model a process or an application where the nodes of the DAG represent the tasks of the process. Prior to the execution of a process in a multicluster environment, the tasks are required to be mapped onto the clusters. In this article, it is shown that the algorithm developed by He et al. [L. He, S.A. Jarvis, D.P. Spooner, D. Bacigalupo, G. Tan, and G.R. Nudd, Mapping DAG-based applications to multiclusters with background workload, Proceedings of the 2005 IEEE International Symposium on Cluster Computing and the Grid, Cardiff, 2005, pp 855–862.] for the multicluster DAG mapping problem can be significantly improved by incorporating the task duplication strategy. The proposed process scheduling algorithm has a time complexity O(| V|²(r+d+1)), where |V| represents the number of tasks; r, the number of clusters; and d, the maximum in-degree of tasks.     

保守PDES中时间管理问题研究

并行离散事件仿真是一种非常有用的分析求解大规模复杂问题的工具,近年来成为仿真界研究热点之一.而并行仿真算法则是并行离散事件仿真中的核心问题,对于具体的应用系统,采用不同的并行仿真算法将导致其仿真性能大的差异.从保守PDES的基础出发,阐述其在时间管理中所遇到的问题,并进行分析和解决,然后给出一种简单的保守PDES系统结构.     

面向服务的网格高性能计算策略

网格技术和Web服务的发展，促成了服务计算的诞生和发展．本文在面向服务的架构下，重新研究传统计算网格下的高性能计算．首先，针舛高性能计算应用的特点，结合面向服务的思想，提出了一种层次资源管理体系结构．其次，分析了适用于网格环境的高性能计算应用的程序结构，并通过有向无循环图（DAG）加以表示．第三，基于上述的资源管理体系结构和高性能计算应用模型，提出了一种改进的动态优先级调度算法．最后，通过仿真实验，分析了提出的算法的性能，实验结果表明提出的算法适用于网格环境，进而验证了本文提出的面向服务的网格高性能计算策略的有效性．     

并行离散事件模拟测试模型研究

世界各国的研究结果表明,并行离散事件模拟在提高模拟速度上表现出较大的潜力。测试模型将为各种并行离散事件模拟算法提供一个综合的平台,方便测试工作的进行,进一步加快测试速度。对测试模型进行研究,介绍其测试内容、测试框架及其可扩展性,并对其应用前景进行展望。     

同构计算环境中一种快速有效的静态任务调度算法

快速有效的调度任务是多处理器计算环境中的一个关键问题．目前任务调度算法中刻画任务依赖关系最流行的模型是DAG,在以前的文献中,提出了一种新的更实际、更普遍的TTIG模型及其相应的MATE算法(基于同构计算环境)．延伸了TTIG模型,并提出基于同构系统的新的算法及两种启发式方法(GBHA1和GBHA2)．GBHA以组的形式尽量消除图中回路,因而能获得任务图的全局信息,具有更好的调度性能．在模拟实验中,将此算法与MATE和其他同构环境中基于DAG的有效调度算法,在不同测试条件下进行了比较,结果显示GBHA在性能上明显优于MATE,与基于DAG模型的调度算法比较而言,在性能方面各有千秋,但在算法时间复杂度方面具有显著的优势．     

A New Grid Scheduler with Failure Recovery and Rescheduling Mechanisms: Discussion and Analysis

Computational Grids (CGs) have become an appealing research area. They suggest a suitable environment for developing large scale parallel applications. CGs integrate a huge mount of distributed heterogeneous resources for constituting a powerful virtual supercomputer. Scheduling is the most important issue for enhancing the performance of CGs. Various strategies have been introduced, including static and dynamic behaviors. The former maps tasks to resources at submission time, while the latter operates at run time. While static scheduling is unsuitable for the dynamic Grid environment, scheduling in CGs is still more complex than the proposed dynamic solutions. This paper introduces a decentralized Adaptive Grid Scheduler (AGS) based on a novel rescheduling mechanism. AGS has several salient properties as it is; hybrid, adaptive, decentralized, and efficient. Also, AGS is a robust mechanism as it has the ability to; (i) detect resource failures, (ii) continue its functionality in spite of the failure existence, then (iii) recover back. Moreover, it integrates both static and dynamic scheduling behaviors. An initial static scheduling map is proposed for an input Direct Acyclic Graph (DAG). However, DAG tasks may be rescheduled if the performance of the allocated resources changes in away that may affect the tasks’ response time. AGS overcomes drawbacks of traditional schedulers by utilizing the mobile agent unique features to enhance the resource discovery and monitoring processes. Experimental results have shown that AGS outperforms traditional Grid schedulers as it introduces a better scheduling efficiency.     

DynaGrid: A dynamic service deployment and resource migration framework for WSRF-compliant applications

Large-scale Grid is a computing environment composed of Internet-wide distributed resources shared by a number of applications. Although WSRF and Java-based hosting environment can successfully deal with the heterogeneity of resources and the diversity of applications, the current Grid systems have several limitations to support the dynamic nature of large-scale Grid.This paper proposes DynaGrid, a new framework for building large-scale Grid for WSRF-compliant applications. Compared to the existing Grid systems, DynaGrid provides three new mechanisms: dynamic service deployment, resource migration, and transparent request dispatching. Two core components, ServiceDoor and dynamic service launcher (DSL), have been implemented as WSRF-compliant Web services to realize DynaGrid, which are applicable to any Java-based WSRF hosting environment. We construct a real testbed with DynaGrid on the Globus Toolkit 4 and evaluate the effectiveness of our framework using two practical applications. The evaluation results show that dynamic service deployment and resource migration in DynaGrid bring many advantages to large-scale Grid in terms of performance and reliability with minimal overhead.     

基于LFT和DAG方式的IPv6路由查找算法

随着网络的不断发展,路由表的信息量也在不断增加,这势必会影响路由转发的效率,影响网络速度.在分析了衡量路由算法优劣的标准后,又通过对现有IPv4和IPv6各种路由算法的分析,尤其是LFT和DAG结构的说明,综合了二者以及LC Trie的优点,提出了算法改进的思想,给出了算法的主要数据结构以及算法执行的流程图,此算法提高了路由查找效率.     

Dependable Grid Workflow Scheduling Based on Resource Availability

Due to the highly dynamic feature, dependable workflow scheduling is critical in the Grid environment. Various scheduling algorithms have been proposed, but seldom consider the resource reliability. Current Grid systems mainly exploit fault tolerance mechanism to guarantee the dependable workflow execution, which, however, wastes system resources. The paper proposes a dependable Grid workflow scheduling system (called DGWS). It introduces a Markov Chain-based resource availability prediction model. Based on the model, a reliability cost driven workflow scheduling algorithm is presented. The performance evaluation results, including the simulation on both parametric randomly generated DAGs and two real scientific workflow applications, demonstrate that compared to present workflow scheduling algorithms, DGWS improves the success ratio of tasks and diminishes the makespan of workflow, so improves the dependability of workflow execution in the dynamic Grid environments.     

Synchronization methods in parallel and distributed discrete-event simulation

This work attempts to provide insight into the problem of executing discrete event simulation in a distributed fashion. The article serves as the state of the art in Parallel Discrete-Event Simulation (PDES) by surveying existing algorithms and analyzing the merits and drawbacks of various techniques. We discuss the main characteristics of existing synchronization methods for parallel and distributed discrete event simulation. The two major categories of synchronization protocols, namely conservative and optimistic, are introduced and various approaches within each category are presented. We also present the latest efforts towards PDES on emerging platforms such as heterogeneous multicore processors, Web services, as well as Grid and Cloud environment.     

A scheduling algorithm for applications in a cloud computing system with communication changes

This paper proposes a scheduling algorithm to solve the problem of task scheduling in a cloud computing system with time‐varying communication conditions. This algorithm converts the scheduling problem with communication changes into a directed acyclic graph (DAG) scheduling problem for existing fuzzy communication task nodes, that is, the scheduling problem for a communication‐change DAG (CC‐DAG). The CC‐DAG contains both computation task nodes and communication task nodes. First, this paper proposes a weighted time‐series network bandwidth model to solve the indefinite processing time (cost) problem for a fuzzy communication task node. This model can accurately predict the processing time of a fuzzy communication task node. Second, to address the scheduling order problem for the computation task nodes, a dynamic pre‐scheduling search strategy (DPSS) is proposed. This strategy computes the essential paths for the pre‐scheduling of the computation task nodes based on the actual computation costs (times) of the computation task nodes and the predicted processing costs (times) of the fuzzy communication task nodes during the scheduling process. The computation task node with the longest essential path is scheduled first because its completion time directly influences the completion time of the task graph. Finally, we demonstrate the proposed algorithm via simulation experiments. The experimental results show that the proposed DPSS produced remarkable performance improvement rate on the total execution time that ranges between 11.5% and 21.2%. In view of the experimental results, the proposed algorithm provides better quality scheduling solution that is suitable for scientific application task execution in the cloud computing environment than HEFT, PEFT, and CEFT algorithms.     

Validating and Scaling the MicroGrid: A Scientific Instrument for Grid Dynamics

Large-scale Grids that aggregate and share resources over wide-area networks present major challenges in understanding dynamic application and resource behavior for performance, stability, and reliability. Accurate study of the dynamic behavior of applications, middleware, resources, and networks depends on coordinated and accurate modeling of all four of these elements simultaneously. We have designed and implemented a tool called the MicroGrid which enables accurate and comprehensive study of the dynamic interaction of applications, middleware, resource, and networks. The MicroGrid creates a virtual Grid environment – accurately modeling networks, resources, the information services (resource and network metadata) transparently. Thus, the MicroGrid enables users, Grid researchers, or Grid operators to study arbitrary collections of resources and networks. The MicroGrid includes the MaSSF online network simulator which provides packet-level accurate, but scalable network modeling. We present experimental results with applications which validate the implementation of the MicroGrid, showing that it not only runs real Grid applications and middleware, but that it accurately models both their and underlying resource and network behavior. We also study a range of techniques for scaling a critical part of the online network simulator to the simulation of large networks. These techniques employ a sophisticated graph partitioner, and a range of edge and node weighting schemes exploiting a range of static network and dynamic application information. The best of these, profile-driven placement, scales well to online simulation of large networks of 6,000 nodes using 24 simulation engine nodes.