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.     

基于微粒群算法的网格工作流优化调度问题的研究

网格工作流中的调度问题是一个复杂且具有挑战性的问题,它影响着网格工作流执行成功与否及效率的高低.针对具有时序和因果约束关系的网格工作流优化调度问题进行了研究,建立了网格工作流的任务调度模型和调度问题的目标模型,并应用微粒群算法来优化网格工作流中任务的调度.实验结果证明该算法优于传统的调度算法.     

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.     

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.     

Adaptive service scheduling for workflow applications in Service-Oriented Grid

When the workflow application is executed in Service-Oriented Grid (SOG), performance issues such as service scheduling should be considered, to achieve high and stable performance in execution. However, most of the prior works on workflow management neither study the performance issues nor provide evaluation methodologies on the performance of Grid Services. Therefore, it is infeasible to apply for the service scheduling problem in SOG. In this paper, we propose and model evaluation metrics for the Grid Service performance. The metrics are extracted based on common properties of Grid Services and are used to quantify and evaluate the performance of an individual Grid Service. With these metrics, we develop a service scheduling scheme with a list scheduling heuristic, to choose proper and optimal Grid Services for tasks in workflow applications. It ensures high performance in the execution of the workflow applications. In addition, we propose a low-overhead rescheduling method, referred to as Adaptive List Scheduling for Service (ALSS), to adapt to the dynamic nature of a grid environment. ALSS provides stable performance for workflow applications, even in abnormal circumstances. Finally, we design an experimental environment with actual traces and perform simulations to quantify the benefits of our approach. Throughout the experiments, we demonstrate that ALSS outperforms conventional scheduling methods. Our scheme produces a scheduling performance that is superior to AHEFT by 50.2%, SLACK by 50.8%, HEFT by 68.3%, MaxMin by 72.0%, MinMin by 71.0%, and Myopic by 69.8%.     

网格环境下费用约束的科学工作流可靠调度算法

网格基础设施是目前科学工作流应用规划、部署和执行的主要支撑环境.然而由于网格资源的自治、动态及异构性,如何在保障用户QoS约束下有效调度科学工作流是一个研究热点.针对费用约束下的科学工作流调度问题,为了提高其执行的可靠性,本文使用随机服务模型描述资源节点的动态服务能力并考虑本地任务负载对资源执行性能的影响,给出一种资源可靠性的评估方法,在此基础上提出一种费用约束下的科学工作流可靠调度算法RSASW.仿真实验结果表明RSASW算法相对于GAIN3,GreedyTime-CD及PFAS算法,对工作流的执行具有很好的可靠性保障.     

Campus Grids Meet Applications: Modeling, Metascheduling and Integration

Air Quality Forecasting (AQF) is a new discipline that attempts to reliably predict atmospheric pollution. An AQF application has complex workflows and in order to produce timely and reliable forecast results, each execution requires access to diverse and distributed computational and storage resources. Deploying AQF on Grids is one option to satisfy such needs, but requires the related Grid middleware to support automated workflow scheduling and execution on Grid resources. In this paper, we analyze the challenges in deploying an AQF application in a campus Grid environment and present our current efforts to develop a general solution for Grid-enabling scientific workflow applications in the GRACCE project. In GRACCE, an application’s workflow is described using GAMDL, a powerful dataflow language for describing application logic. The GRACCE metascheduling architecture provides the functionalities required for co-allocating Grid resources for workflow tasks, scheduling the workflows and monitoring their execution. By providing an integrated framework for modeling and metascheduling scientific workflow applications on Grid resources, we make it easy to build a customized environment with end-to-end support for application Grid deployment, from the management of an application and its dataset, to the automatic execution and analysis of its results.The work has been performed as part of the University of Houston’s Sun Microsystems Center of Excellence in Geosciences [38].     

一种基于两级DAG模型的MapReduce工作流异构调度算法

MapReduce编程模型被广泛应用于大数据处理平台,而一个有效的任务调度算法对模型的运行效率至关重要。将MapReduce工作流的Map和Reduce阶段分别拆解为若干个有先后序限定关系的作业,每个作业再拆解为多个任务。之后基于计算集群的可用资源和任务异构性,构建面向作业和任务的2级有向无环图(DAG)模型,同时提出基于2级优先级排序的异构调度算法2-MRHS。算法的第1阶段进行优先级排序,即对作业和任务分别进行优先权值计算,再汇总得到任务的调度队列;第2阶段进行任务分配,即基于最快完成时间将每个任务所包含的数据块子任务分配给最适合的计算结点。采用大批量随机生成的DAG模型进行实验,结果表明与其他相关算法相比,本文算法有更短的调度长度(makespan)且更加稳定。     

A probabilistic and adaptive scheduling algorithm using system-generated predictions for inter-grid resource sharing

Rapid advancement and more readily availability of Grid technologies have encouraged many businesses and researchers to establish Virtual Organizations (VO) and make use of their available desktop resources to solve computing intensive problems. These VOs, however, work as disjointed and independent communities with no resource sharing between them. We, in previous work, have proposed a fully decentralized and reconfigurable Inter-Grid framework for resource sharing among such distributed and autonomous Grid systems (Rao et al. in ICCSA, [2006]). The specific problem that underlies in such a collaborating Grids system is scheduling of resources as there is very little knowledge about availability of the resources due to the distributed and autonomous nature of the underlying Grid entities. In this paper, we propose a probabilistic and adaptive scheduling algorithm using system-generated predictions for Inter-Grid resource sharing keeping collaborating Grid systems autonomous and independent. We first use system-generated job runtime estimates without actually submitting jobs to the target Grid system. Then this job execution estimate is used to predict the job scheduling feasibility on the target system. Furthermore, our proposed algorithm adapted itself to the actual resource behavior and performance. Simulation results are presented to discuss the correctness and accuracy of our proposed algorithm.

Heuristic solutions to resource allocation in grid computing: a natural approach

Grid computing connects heterogeneous resources to achieve the illusion of being a single available entity. Charging for these resources based on demand is often referred to as utility computing, where resource providers lease computing power with varying costs based on processing speed. Consumers using this resource have time and cost constraints associated with each job they submit. Determining the optimal way to divide the job among the available resources with regard to the time and cost constraints is tasked to the Grid Resource Broker (GRB). The GRB must use an optimization algorithm that returns an accurate result in a timely manner. The genetic algorithm and the simulated annealing algorithm can both be used to achieve this goal, although simulated annealing outperforms the genetic algorithm for use by the GRB. Determining optimal values for the variables used in each algorithm is often achieved through trial and error, and success depends upon the solution domain of the problem.

基于GAPSO混合算法的网格工作流调度研究

网格工作流调度关注大规模的资源和任务调度,是一个复杂且具有挑战性的问题,它影响着网格工作流执行成功与否以及效率的高低。提出了基于遗传粒子群(GAPSO)的混合算法,引用了特殊的适应度函数,设定了动态的交叉和变异概率,并提出了动态切换算法的方法。结合各自算法的优势,在算法运行初期利用遗传算法的全局搜索能力进行优化搜索,在后期利用粒子群较强的局部搜索能力加快收敛速度。仿真结果表明该算法在执行时间方面有一定的优越性,能更有效地解决网格工作流调度问题。     

Dataflow detection and applications to workflow scheduling

In high‐performance computing (HPC)textitworkloads (i.e. the set of computations to be completed), the same computational workflow of jobs (e.g. a Pipeline, a Fork&Join, or a Lattice graph) may be applied to different input files and parameters. Each of these workflow instances has the same workflow shape, but accesses (possibly) separate input, intermediate, and output files. Therefore, the selective isolation of each workflow instance can be important for maximizing scheduling flexibility and performance. However, in practice, realizing this benefit is not obvious due to a variety of problems and constraints. For example, the unmediated interaction of different workflow instances can lead to a problem of filename conflicts between concurrent workflow instances overwriting common files, which, for a control‐flow driven batch scheduler, may result in either unsafe computation of the multiple instances in the same sub‐directory or storage overheads when multiple directories are used. We propose a novel approach of selectively coupling and integrating job schedulers and file systems, known as a Workflow‐aware File System (WaFS), with two major benefits. First, separate namespaces can be constructed on a per‐instance basis to maximize the concurrency of workflow instances, despite filename conflicts, while minimizing storage overhead. Second, exploiting inferred dataflow information, trade‐offs can be made between makespan and storage overhead while maintaining correctness. Through a simulation‐based study, we have shown the potential benefits of WaFS to job concurrency and we have characterized the trade‐offs that can be made between storage overhead and performance. New scheduling policies, Versioned Namespace (VNS), Overwrite‐Safe Concurrency (OSC) and hybrids, are made possible by WaFS, with different advantages and disadvantages. Copyright © 2011 John Wiley & Sons, Ltd.     

Dimensioning and on-line scheduling in Lambda Grids using divisible load concepts

Due to the large amounts of data required to be processed by the typical Grid job, it is conceivable that the use of optical transport networks in Grid deployment (hence the term “Lambda Grid”) will increase. The exact topology of the interconnecting network is obtained by solving a dimensioning problem, and the outcome of this strongly depends on both the expected workload characteristics and Grid scheduling policy. Solving this combined scheduling and dimensioning problem using straightforward ILP modelling is cumbersome; however, for steady-state Grid operation, Divisible Load Theory (DLT) can yield scalable formulations of this problem. In this paper, the on-line hierarchical scheduling on a lambda Grid of workload approaching the Grid’s capacity in a two-tier Grid mode of operation is studied. A number of these algorithms are goal-driven, in the sense that target per-resource goals are obtained from the off-line solution to the Divisible Load model. We compare these on-line multiresource scheduling policies for different workloads, Grid interconnection topologies and Grid parameters. We show that these algorithms perform well in the studied scenarios when compared to a fully centralized scheduling algorithm.

一种云工作流任务调度能效优化算法

工作流任务执行时带来的高能耗不仅会增加云资源提供方的经济成本,而且会降低云系统的可靠性。为了满足截止时间的同时,降低工作流执行能耗,提出一种工作流能效调度算法CWEES。算法将能效优化调度划分为三个阶段：初始任务映射、处理器资源合并和任务松驰。初始任务映射旨在通过任务自底向上分级排序得到任务调度初始序列,处理器资源合并旨在通过重用松驰时间合并相对低效率的处理器,降低资源使用数量,任务松驰旨在为每个任务重新选择带有合适电压/频率等级的最优目标资源,在不违背任务顺序和截止时间约束前提下降低工作流执行总能耗。通过随机工作任务模型对算法的性能进行了仿真实验分析。结果表明,CWEES算法不仅资源利用率更高,而且可以在满足截止时间约束下降低工作流执行能耗,实现执行效率与能耗的均衡。     

分布式系统下的DAG任务调度研究综述

近年来随着网格、云计算工作流等分布式计算技术的发展,关于DAG(有向无环图)模型任务在分布式系统环境下的调度问题逐渐成为备受关注的研究热点。根据最新研究进展,对分布式系统下的DAG任务调度问题和有关技术进行了研究与讨论,主要包括四个方面:系统地描述了分布式系统和异构分布式系统的有关概念,异构分布式系统下的DAG任务调度问题、调度模型及其典型应用;对现有分布式系统下DAG任务调度的研究按照不同的方式进行了分类;探讨了多DAG共享异构分布式资源调度的研究现状;讨论了目前多DAG共享异构分布式资源调度研究存在的问题和未来可能的研究方向。     

Near‐optimal dynamic priority scheduling strategy for instance‐intensive business workflows in cloud computing

Utilization of cloud computing resources has made a fast growth in e‐business. Business and government agencies often need to handle large volume of service requests, the so‐called instance‐intensive business processes in a constrained period. On‐time completion for instance‐intensive business processes within the constrained time is a very important issue. In the past few years, traditional optimal task scheduling has been well researched and proven to be a nondeterministic polynomial (NP) time–complete problem. So many heuristic and metaheuristic algorithms are put forward to solve the issue with near‐optimal solutions. However, most of them just treat a single workflow instance as a multistep task without considering that steps within a task can be different types of activities. To explain multistep features of business workflows, a typical motivating instance‐intensive business example of security exchange and a multistep scheduling model for business workflows are introduced in this paper. Then our near‐optimal dynamic priority scheduling (DPS) strategy is proposed on the basis of the idea of Min‐Min heuristic algorithm and greedy philosophy. Compared to the first come first served and constrained Min‐Min by makespan and standard deviation, DPS can make a more optimized choice in each round of scheduling towards overall outcome. To show the effectiveness of DPS, theoretical minimum execution time (MET_theory) is used as a benchmark for evaluation based on simulation. The results show that the ratios between MET_theory and DPS are more than 98.5% by scheduling different orders of magnitude tasks from 1000 to 1 000 000. In particular, the ratio between MET_theory and DPS is nearly 99.9% with 1 000 000 tasks, which means that our DPS can get the near‐optimal result when scheduling large number of tasks.     

自适应邻域的多目标网格任务调度算法研究

针对网格计算中的多目标网格任务调度问题,提出了一种基于自适应邻域的多目标网格任务调度算法。该算法通过求解多个网格任务调度目标函数的非劣解集,采用自适应邻域的方法来保持网格任务调度多目标解集的分布性,尝试解决网格任务调度中多目标协同优化问题。实验结果证明,该算法能够有效地平衡时间维度和费用维度目标,提高了资源的利用率和任务的执行效率,与Min-min和Max-min算法相比具有较好的性能。     

Minimizing total weighted completion time with uncertain data: A stability approach

A single-machine scheduling problem is investigated provided that the input data are uncertain: The processing time of a job can take any real value from the given segment. The criterion is to minimize the total weighted completion time for the n jobs. As a solution concept to such a scheduling problem with an uncertain input data, it is reasonable to consider a minimal dominant set of job permutations containing an optimal permutation for each possible realization of the job processing times. To find an optimal or approximate permutation to be realized, we look for a permutation with the largest stability box being a subset of the stability region. We develop a branch-and-bound algorithm to construct a permutation with the largest volume of a stability box. If several permutations have the same volume of a stability box, we select one of them due to one of two simple heuristics. The efficiency of the constructed permutations (how close they are to a factually optimal permutation) and the efficiency of the developed software (average CPU-time used for an instance) are demonstrated on a wide set of randomly generated instances with 5 ≤ n ≤ 100.     

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。     

Modelling and solving grid resource allocation problem with network resources for workflow applications

A problem of allocating resources of a grid to workflow applications is considered. The problem consists, generally, in allocating distributed grid resources to tasks of a workflow in such a way that the resource demands of each task are satisfied. Grid resources are divided into computational resources and network resources. Computational tasks and transmission tasks of a workflow are distinguished. We present a model of the problem, and an algorithm for finding feasible resource allocations. A numerical example is included, showing the importance of the resource allocation phase on a grid. Some conclusions and directions for future research are given.