网格环境下工作流的费用-时间调度算法

开放网格服务架构（OGSA）和计算经济模型的提出,使得动态的、不同QoS的服务支持下的资源调度成为一个复杂且具有挑战性的问题。该文提出了网格环境下基于费用-时间的工作流调度算法,该算法采用动态资源选择策略适应网格计算环境下的动态性和自治性。在追求较小的工作流完成时间的同时,对费用进行了优化。模拟结果显示该调度算法符合计算网格的复杂环境,能够更好地满足不同用户的实际需要。     

Incentive-Based Scheduling for Market-Like Computational Grids

A sustainable, market-like computational grid has two characteristics: it must allow resource providers and resource consumers to make autonomous scheduling decisions; and both parties of providers and consumers must have sufficient incentives to stay and play in the market. In this paper, we formulate this intuition of optimizing incentives for both parties as a dual-objective scheduling problem. The two objectives identified are to maximize the success rate of job execution, and to minimize fairness deviation among resources. The challenge is to develop a grid scheduling scheme that enables individual participants to make autonomous decisions while produces a desirable emergent property in the grid system, namely, the two objectives are achieved simultaneously. We present an incentive-based scheduling scheme which utilizes a peer-to-peer decentralized scheduling framework, a set of local heuristic algorithms, and three market instruments of job announcement, price, competition degree. The performance of this scheme is evaluated via extensive simulation using synthetic and real workloads. The results show that our approach outperforms other scheduling schemes in optimizing incentives for both consumers and providers, leading to highly successful job execution and fair profit allocation.     

计算服务网格中基于服务聚类的元任务调度算法

在尊重网格资源本地调度策略前提下,提出一种基于云模型的动态服务能力评估方法;根据动态性能评估尺度对服务进行聚类,提出了一种基于PSO的自适应的服务动态聚类方法,将提供相同或相似QoS的服务划分到同一个服务簇中,从而缩小了任务调度的问题规模;基于服务动态聚类提出了一种元任务调度算法,理论分析该算法降低了不聚类调度算法的复杂度.实验结果表明本文提出的调度算法在时间复杂度与用户QoS保障方面优于以前提出的调度算法.     

基于模糊粒子群优化的计算网格工作调度算法

网格计算是利用网络把分散的计算资源组织起来解决复杂问题的计算模式,工作调度是待解决的主要问题之一。本文提出一种基于模糊粒子群优化的网格计算工作调度算法,该算法利用模糊粒子群优化动态地产生网格计算工作调度的优化方案,使现有计算资源完成所有工作的时间最小化。实验结果表明,与基于遗传算法、模拟退火、蚁群算法的工作调度方法相比,所提出的算法在时间和精度上具有一定的优势。     

基于计算物流与计算智能的多码头泊位堆场联合调度

针对同一地区邻近集装箱码头往往物流功能相似、货源腹地重叠、无序竞争突出和资源利用率较低等特点, 本文重点探讨了隶属于同一组织内且位置相邻多集装箱码头的泊位-堆场一体化计划调度(multiple container terminal tactical berth and yard incorporate integrative scheduling, MCT-TBY-IIS)问题. 基于计算物流, 利用多重多背包问题将MCT-TBY-IIS抽象和分解为考虑泊位水深约束和出口集装箱可转港作业的多码头动态连续泊位分配和多码头周期滚动堆场分配两个中度耦合子问题, 进而在计算物流面向问题探索的思想下, 提出了面向层次嵌套结构的二阶段改进帝国竞争算法(hierarchical nesting oriented two-stage improved imperialist competitive algorithm, HNO-TSI-ICA)对MCT-TBY-IIS进行求解优化. 最后, 面向我国东南沿海的典型多码头联合作业实例, 遴选出面向帝国兴替的双同化帝国竞争改进算法和面向0-1背包问题的二进制帝国竞争算法组合应用于HNO-TSI-ICA, 其在求解MCT-TBY-IIS时效果较好, 且堆场作业子系统目标成本的结构较稳定, 其不受计划期内港口负荷和计划周期长度的影响, 其中, 出口箱区集装箱水平运输成本的贡献度在堆场作业子目标成本的比重最大, 稳定在83%左右. 通过对MCT-TBY-IIS的建模与优化, 可以发现多码头联合作业模式有较好的潜力帮助同一组织内邻近的多码头降本增效和提高核心资源的利用率.     

Scheduling for Responsive Grids

Grids are facing the challenge of seamless integration of the Grid power into everyday use. One critical component for this integration is responsiveness, the capacity to support on-demand computing and interactivity. Grid sched uling is involved at two levels in order to provide responsiveness: the policy level and the implementation level. The main contributions of this paper are as follows. First, we present a detailed analysis of the performance of the EGEE Grid with respect to responsiveness. Second, we examine two user-level schedulers located between the general scheduling layer and the application layer. These are the DIANE (distributed analysis environment) framework, a general-purpose overlay system, and a specialized, embedded scheduler for gPTM3D, an interactive medical image analysis application. Finally, we define and demonstrate a virtualization scheme, which achieves guaranteed turnaround time, schedulability analysis, and provides the basis for differentiated services. Both methods target a brokering-based system organized as a federation of batch-scheduled clusters, and an EGEE implementation is described.     

计算网格工作负荷的建模

为评估计算网格中的作业调度,建立了网格工作负荷模型。在不同的节点,作业的运行时间不同;在不同的节点之间,作业的迁移开销不同。定义了不依赖网格资源性能的纯运行时间和纯迁移开销。借鉴并行计算机的工作负荷模型,可得到并行度、纯运行时间和到达间隔的分布。构建了作业提交位置、纯迁移开销、纯运行时间估计因子、完成期限的分布。应用实例表明,由网格工作负荷模型可获得 各种工作负荷,支持对作业调度的全面评估。     

面向实例方面处理的工作流动态调度优化方法

针对现实业务过程对实例方面处理的需求,建立面向实例方面处理的工作流动态调度优化模型,并提出了相应的优化方法.该方法利用蚁群优化算法的特点直接构建可行解,利用分组浪费时间与分组浪费费用的概念来设计启发式信息,同时优化最小化活动实例的总停留时间与总执行费用这两个目标函数,最终产生一组满足约束条件的Pareto优化调度方案.实验结果说明了算法的有效性.     

基于效益函数的网格任务调度算法

在动态、异构、分布广泛的网格环境中,对资源的调度是一个非常复杂而重要且具有挑战性的问题。本文针对网格环境中的动态性特点,特别是用户QoS要求的动态变化性,提出了一种基于效益函数的网格任务调度算法,并采用GridSim模拟器分别对该调度算法和模拟器自带的代价最优和时间最优的网格任务调度算法进行模拟。实验的结果表明：该调度算法更能体现用户对QoS要求的动态变化;在系统完成相同数量的网格任务时,消耗相同时间的情况下,该调度算法在代价上优于基于时间优化的调度算法;而花费相同预算的情况下,在时间上优于基于代价优化的调度算法。     

Fair Scheduling Algorithms in Grids

In this paper, we propose a new algorithm for fair scheduling, and we compare it to other scheduling schemes such as the earliest deadline first (EDF) and the first come first served (FCFS) schemes. Our algorithm uses a max-min fair sharing approach for providing fair access to users. When there is no shortage of resources, the algorithm assigns to each task enough computational power for it to finish within its deadline. When there is congestion, the main idea is to fairly reduce the CPU rates assigned to the tasks so that the share of resources that each user gets is proportional to the users weight. The weight of a user may be defined as the users contribution to the infrastructure or the price he is willing to pay for services or any other socioeconomic consideration. In our algorithms, all tasks whose requirements are lower than their fair share CPU rate are served at their demanded CPU rates. However, the CPU rates of tasks whose requirements are larger than their fair share CPU rate are reduced to fit the total available computational capacity in a fair manner. Three different versions of fair scheduling are adopted in this paper: the simple fair task order (SFTO), which schedules the tasks according to their respective fair completion times, the adjusted fair task order (AFTO), which refines the SFTO policy by ordering the tasks using the adjusted fair completion time, and the max-min fair share (MMFS) scheduling policy, which simultaneously addresses the problem of finding a fair task order and assigning a processor to each task based on a max-min fair sharing policy. Experimental results and comparisons with traditional scheduling schemes such as the EDF and the FCFS are presented using three different error criteria. Validation of the simulations using real experiments of tasks generated from 3D image- rendering processes is also provided. The three proposed scheduling schemes can be integrated into existing grid computing architectures.     

成本约束的网格工作流时间优化方法

针对成本约束有向无环图DAG(directed acyclic graph)表示的网格工作流完工时间最小化问题,提出两个基于优先级规则的迭代启发算法.算法利用并行活动特征定义正向分层和逆向分层两个概念,将其分别引入最大收益规则MP(maximum profit),得到正分层最大收益规则MPTL(maximum profit with top level) 和逆分层最大收益规则MPBL(maximum profit with bottom level).两规则每次迭代尽量以完工时间的最小增加换取总费用的最大降低,逐步将分层初始解构造为满足成本约束的可行解.模拟结果表明,两规则在获得较少迭代次数和运行时间的同时,能显著改进MP规则的平均性能,且MPBL优于MPTL.     

Negotiation-Based Scheduling of Scientific Grid Workflows Through Advance Reservations

In its broadest sense, scheduling of Grid applications can be viewed as a negotiation process between a scheduling service optimising user-centric objectives such as execution time, and a resource manager optimising provider-centric metrics such as resource utilisation or fairness. In this paper we enhance an existing list scheduling algorithm designed for minimising the workflow makespan with advance reservation-based negotiation functionality. As an instantiation of the new negotiation phase, we investigate two advance reservation functionality from the resource provider perspective: attentive and progressive. We illustrate through real-world experiments a two-fold benefit of our approach: improved execution predictability from the user’s perspective, and higher resource utilisation fairness through a new progressive allocation strategy from the provider’s perspective.     

Cost-Time Efficient Scheduling Plan for Executing Workflows in the Cloud

The emergence of Cloud Computing as a model of service provisioning in distributed systems instigated researchers to explore its pros and cons on executing different large scale scientific applications, i.e., Workflows. One of the most challenging problems in clouds is to execute workflows while minimizing the execution time as well as cost incurred by using a set of heterogeneous resources over the cloud simultaneously. In this paper, we present, Budget and Deadline Constrained Heuristic based upon Heterogeneous Earliest Finish Time (HEFT) to schedule workflow tasks over the available cloud resources. The proposed heuristic presents a beneficial trade-off between execution time and execution cost under given constraints. The proposed heuristic is evaluated for different synthetic workflow applications by a simulation process and comparison is done with state-of-art algorithm i.e. BHEFT. The simulation results show that our proposed scheduling heuristic can significantly decrease the execution cost while producing makespan as good as the best known scheduling heuristic under the same deadline and budget constraints.     

基于混合粒度冲突检测的事务工作流调度算法

事务工作流由若干个平面事务组成,其执行满足松弛原子性.由于组成事务工作流的平面事务具有不同的完成特性,为了防止不可串行化的执行,现有的调度算法通常只允许一个活动工作流执行不可补偿事务,这大大限制了并发度.定义了基于事务类型和事务实例两种粒度的冲突关系,并提出了一种基于这两种粒度冲突检测的调度算法,保证了并发事务工作流的可串行化和可恢复执行.该算法从两个方面提高了并发度:一方面通过事务实例之间(细粒度)的冲突检测减少了工作流冲突的概率;另一方面通过事务类型之间(粗粒度)的冲突预测,允许多个将来不冲突的工作流执行不可补偿事务.     

基于串归约的网格工作流费用优化方法

针对截止期限约束下有向无环图DAG(directed acyclic graph)表示的工作流费用优化问题,提出两个新的费用优化算法:时间约束的前向串归约算法FSRD(forward serial reduction within deadline)和时间约束的后向串归约算法BSRD(backward serial reduction within deadline).算法利用DAG图中串行活动特征给出串归约概念;基于分层算法对串归约组的时间窗口重定义,并提出动态规划的求解策略实现组内费用的最优化.两种归约算法综合考虑DAG图中活动的串并特征,改变分层算法中仅对单一活动的费用优化策略,实现了串归约组的时间收集和最优利用.模拟实验结果表明: BSRD和FSRD能够显著改进相应分层算法的平均性能,且BSRD优于FSRD.     

An Energy-Aware Heuristic Scheduling for Data-Intensive Workflows in Virtualized Datacenters

With the development of cloud computing, more and more data-intensive workflows have been deployed on virtualized datacenters. As a result, the energy spent on massive data accessing grows rapidly. In this paper, an energy-aware scheduling algorithm is proposed, which introduces a novel heuristic called Minimal Data-Accessing Energy Path for scheduling data-intensive workflows aiming to reduce the energy consumption of intensive data accessing. Extensive experiments based on both synthetical and real workloads are conducted to investigate the effectiveness and performance of the proposed scheduling approach. The experimental results show that the proposed heuristic scheduling can significantly reduce the energy consumption of storing/retrieving intermediate data generated during the execution of data-intensive workflow. In addition, it exhibits better robustness than existing algorithms when cloud systems are in presence of I/O- intensive workloads.     

Towards a Powerful European DCI Based on Desktop Grids

Service Grids like the EGEE Grid can not provide the required number of resources for many VOs. Therefore extending the capacity of these VOs with volunteer or institutional desktop Grids would significantly increase the number of accessible computing resources that can particularly advantageously be exploited in case of parameter sweep applications. This objective has been achieved by the EDGeS project that built a production infrastructure enabling the extension of gLite VOs with several volunteer and institutional desktop Grids. The paper describes the technical solution of integrating service Grids and desktop Grids and, the actual EDGeS production infrastructure. The main objectives and current achievements of the follow-up EDGI project have also been described showing how the existing EDGeS infrastructure can be further extended with clouds.     

基于扩展关键活动的工作流调度算法

服务资源分配和调度在开放网格服务架构环境中是一个复杂且具有挑战性的问题.为适应网格计算环境下服务资源的动态性和自治性,提出一个基于扩展关键活动的工作流调度算法,采用动态选择所需服务资源的策略,工作流完成时间和所需成本得到一定程度的平衡.实验结果表明该调度算法在完成时间、成本等方面可得到较满意的结果,能更好地满足用户实际需要.     

一种基于双向拍卖机制的计算网格资源分配方法

针对计算网格资源的特点以及运用经济机制进行网格资源管理所具有的灵活性及有效性,提出一种改进的基于双向拍卖机制的网格资源分配方法.首先,描述了基于双向拍卖机制的资源分配框架,整个系统由买方、卖方和计算资源经纪人组成.然后,针对网格中的CPU资源,提出一种改进的双向拍卖机制,采用统一拍卖方式,可以灵活调节交易双方的付费.进而,分析了该双向拍卖机制满足优势策略激励相容、预算平衡以及个人理性的特点,并定义了拍卖机制的效率.最后,通过实验分析了双向拍卖分配机制的效率.     

Game-Theoretic Approach for Load Balancing in Computational Grids

Load balancing is a very important and complex problem in computational grids. A computational grid differs from traditional high-performance computing systems in the heterogeneity of the computing nodes, as well as the communication links that connect the different nodes together. There is a need to develop algorithms that can capture this complexity yet can be easily implemented and used to solve a wide range of load-balancing scenarios. In this paper, we propose a game-theoretic solution to the grid load-balancing problem. The algorithm developed combines the inherent efficiency of the centralized approach and the fault-tolerant nature of the distributed, decentralized approach. We model the grid load-balancing problem as a noncooperative game, whereby the objective is to reach the Nash equilibrium. Experiments were conducted to show the applicability of the proposed approaches. One advantage of our scheme is the relatively low overhead and robust performance against inaccuracies in performance prediction information.