混合算法在网格任务调度中的应用研究

研究网格任务优化调度问题,针对需求的复杂和网格系统具有异构性和动态性,导致网络任务调度过程相当困难.传统调度算法调度效率低、资源负载不平衡.为了提高任务调度效率,降低资源负载不平衡性,提出一种混合的网格任务调度优化算法.首先采用遗传算法全局搜索能力快速形成初始解,然后将遗传算法的调度结果作为蚁群算法的初始信息素分布,最后利用蚁群算法所正反馈性机制迅速地形成任务调度的最优解.仿真结果表明,混合算法减少网格任务调度系统任务完成时间,提高了任务调度效率,为网格设计提供了依据.     

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。     

基于量子蚁群算法的网格任务调度研究

任务调度策略是网格计算的核心问题。在系统任务调度和资源分配中,提出一种基于量子蚁群算法的任务调度策略。算法将量子计算与蚁群算法相融合,通过对蚁群进行量子化编码并采用量子旋转门及非门操作,实现对任务自适应启发式的分配和优化。算法有效增强了种群的多样性、克服了遗传算法和蚁群算法的早熟收敛和退化现象。仿真实验中,分别与基于遗传算法和基于蚁群算法的任务调度策略相对比,结果表明算法有效缩短了任务调度的时间跨度,增强了网格系统的性能。     

混合蚁群算法在网格计算任务调度中的应用

网格环境下的资源分配与任务调度问题已经被证明了是一个NP难题,而传统的任务调度算法很难对大量的异构的、动态的网格任务进行有效的调度。本文提出了一种任务调度模型,并且在该模型中采用混合蚁群算法,该算法以信息素为启发,引导蚂蚁选择最优资源。蚂蚁选择资源之后不仅进行信息素的整体更新,还要求预分配网格资源时进行信息素的局部更新。模拟实验表明该算法是一种快速,有效,负载更均衡的算法。     

具有模糊多目标的网格任务调度算法

在异构的网格计算平台上,网格中有用户、资源管理员、组织管理者等实体,这些实体对网格的管理、使用、维护、安全性、可靠性等目标都提出了要求,并且这些目标有时是不可量化的。针对具有模糊多目标网格计算的任务调度问题,提出模糊多目标网格任务调度模型,使用模糊化等式对多目标进行模糊处理,给出求解该模型的模糊化定理,并对该定理进行证明。利用差分优化算法无需目标函数连续可微的特点,提出使用模糊差分优化算法完成模糊多目标的网格任务调度。实验结果表明,模糊差分优化算法较现有算法在执行时间上处于劣势,但在可靠性、安全性和丢失任务数三个指标上要优于现有算法。     

融合安全的网格依赖任务调度双目标优化模型及算法

为了解决异构网格环境下依赖任务调度问题面临的安全威胁,综合考虑网格资源节点的固有安全性和行为安全性,分别构建了一个网格资源节点身份可靠性度量函数和行为表现信誉度评估策略.同时,为了确立任务安全需求与资源节点安全属性之间的隶属关系,定义了安全效益隶属度函数,从而建立一个网格任务调度的安全融合模型.以此为基础,提出一个时间-安全驱动的双目标优化网格依赖任务调度模型.为了求解该模型,处理任务间约束关系时引入深度值和关联耦合度的排序定义,再结合网格任务调度问题的具体特点,重新定义和设计新的粒子进化方程.同时,基于均匀分布向量和粒子浓度定义了选择策略,从而提出一种双目标优化的网格依赖任务调度粒子群进化算法,并运用概率论的有关知识证明算法的收敛性.最后,对所提出的离散粒子群进化算法进行了多角度分析和大规模仿真实验,其仿真结果表明,该算法与同类算法相比,不仅具有较好的收敛速度和单目标优化性能,而且在任务调度长度和安全满意度方面具有更好的双目标优化综合性能.     

一种基于蚁群算法的网格任务调度方法

网格资源具有动态变化,广域分布及系统异构的特性,如何分配调度这些资源成为网格计算研究领域一个重要研究课题。国内外在网格任务调度研究上已经做了大量工作,但是这些算法大多是基于计算网格的,不能很好的适应服务网格环境下存在任务相关性的调度,同时在适应网格的动态性、异构性上也存在不足。针对目前网格调度机制存在的问题,提出了一种基于蚁群算法的服务网格任务动态调度方法,仿真实验结果表明该算法具有较好的性能和自适应性。     

基于多群智能算法的云计算任务调度策略

为提高云计算任务调度的服务质量(QoS),提出一种多群智能算法的云计算任务调度策略。首先利用全局搜索能力强的遗传算法快速找到云计算任务调度问题的较优解,然后将较优解转换成蚁群优化算法的初始信息素,最后通过蚂蚁间的信息交流和反馈找到云计算任务调度的全局最优解。以CloudSim为仿真平台进行了模拟实验,结果表明,与同类算法相比,多群智能算法不仅大幅提高了云计算任务调度效率,而且减少了处理请求任务的平均完成时间。     

一种基于改进蚁群算法的网格任务调度算法

在网格计算中,任务调度是影响系统性能和服务质量的重要问题。文章在考虑截止时间和花费两项用户QoS需求的基础上,提出了一种基于蚁群算法的网格任务调度算法。该算法先采用GC（Greedy Cost-Time Distribution）算法求解,将所得解转化为蚁群算法的初始信息素分布,然后利用蚁群算法获得调度解。模拟实验结果表明：在网格环境下,该调度算法具有明显的优势。     

多目标蚁群优化网格调度算法

提出基于蚁群算法的网格调度算法,优化作业完成时间。同时局部升级和全局升级采用不同策略,解决资源负载均衡问题,满足网格的多目标优化。最后通过Gridsim仿真环境和其他算法进行比较分析。     

Resource scheduling with conflicting objectives in grid environments: Model and evaluation

The paper is to consider resource scheduling with conflicting objectives in the grid environment. The objectives of the grid users, the grid resources and the grid system clash with each other. Grid users want to access enough system resources to achieve the desired level of quality of service (QoS). Resource providers pay more attention to the performance of their resources. Our resource scheduling employs market strategies to determine which jobs are executed at what time on which resources and at what prices. A grid resource provider uses its utility function to maximize its profit and a grid user uses its utility function to complete tasks while minimizing its spending. The paper proposes grid system objective optimization scheduling that provides a joint optimization of objectives for both the resource provider and grid user, which combines the benefits of both resource provider objective optimization and user objective optimization. Experiments are designed to study the performances of three resource-scheduling optimization algorithms. Performance metrics are classified into efficiency metrics, utility metrics and time metrics.     

Toward balanced and sustainable job scheduling for production supercomputers

Job scheduling on production supercomputers is complicated by diverse demands of system administrators and amorphous characteristics of workloads. Specifically, various scheduling goals such as queuing efficiency and system utilization are usually conflicting and thus need to be balanced. Also, changing workload characteristics often impact the effectiveness of the deployed scheduling policies. Thus it is challenging to design a versatile scheduling policy that is effective in all circumstances. In this paper, we propose a novel job scheduling strategy to balance diverse scheduling goals and mitigate the impact of workload characteristics. First, we introduce metric-aware scheduling, which enables the scheduler to balance competing scheduling goals represented by different metrics such as job waiting time, fairness, and system utilization. Second, we design a scheme to dynamically adjust scheduling policies based on feedback information of monitored metrics at runtime. We evaluate our design using real workloads from supercomputer centers. The results demonstrate that our scheduling mechanism can significantly improve system performance in a balanced, sustainable fashion.     

An efficient grid-scheduling strategy based on a fuzzy matchmaking approach

Computational grids have become an appealing research area as they solve compute-intensive problems within the scientific community and in industry. A grid computational power is aggregated from a huge set of distributed heterogeneous workers; hence, it is becoming a mainstream technology for large-scale distributed resource sharing and system integration. Unfortunately, current grid schedulers suffer from the haste problem, which is the schedule inability to successfully allocate all input tasks. Accordingly, some tasks fail to complete execution as they are allocated to unsuitable workers. Others may not start execution as suitable workers are previously allocated to other peers. This paper is the first to introduce the scheduling haste problem. It also presents a reliable grid scheduler. The proposed scheduler selects the most suitable worker to execute an input grid task using a fuzzy inference system. Hence, it minimizes the turnaround time for a set of grid tasks. Moreover, our scheduler is a system-oriented one as it avoids the scheduling haste problem. Experimental results have shown that the proposed scheduler outperforms traditional grid schedulers as it introduces a better scheduling efficiency.     

HITGRID:基于动态规划技术的网格任务调度中间件

随着计算规模不断扩大,人们对并行计算调度的性能要求也不断提高。网格技术的出现,解决了计算资源的共享性、异构性、规模可扩展性、鲁棒性以及安全性等方面的问题,但同时也在资源调度方面带来了新的挑战。HITGRID是一个基于Globus网格工具平台上的网格调度中间件,它接收用户提交的计算任务,针对用户对计算资源提出的要求,在网格环境中查找符合条件的资源进行调度。文中讨论了将NWS预测技术封装为标准网格资源信息的方法,以及资源调度的性能模型,给出了调度中资源选择的策略。最后通过一个计算大规模N体问题的实例表明笔者的工作是有成效的。     

An efficient system-oriented grid scheduler based on a fuzzy matchmaking approach

Computational grids have become an appealing research area as they solve compute-intensive problems within the scientific community and in industry. A Grid computational power is aggregated from a huge set of distributed heterogeneous workers; hence, it is becoming a mainstream technology for large-scale distributed resource sharing and system integration. Unfortunately, current grid schedulers suffer from the haste problem, which is the schedule inability to successfully allocate all input tasks. Accordingly, some tasks fail to complete execution as they are allocated to unsuitable workers. Others may not start execution as suitable workers are previously allocated to other peers. This paper is the first to introduce the scheduling haste problem. It also presents a reliable grid scheduler. The proposed scheduler selects the most suitable worker to execute an input grid task using a fuzzy inference system. Hence, it minimizes the turnaround time for a set of grid tasks. Moreover, our scheduler is a system-oriented one as it avoids the scheduling haste problem. Experimental results have shown that the proposed scheduler outperforms traditional grid schedulers as it introduces a better scheduling efficiency.     

用爬山法实现无中心式网格调度

为方便网格资源的扩展,网格调度应当是无中心的.为在尽可能多的计算资源中为单地点作业优化资源选择,这里采用了爬山算法.当一个网格调度器收到一个单地点作业,爬山法被激活,根据网格调度器之间的相邻关系为作业找出最适合的计算系统,这里每个计算系统的适合度用预测的作业响应时间表示.实验模拟了无中心式网格调度与计算系统之间的性能差别,每个计算系统的本地调度采用保守式装填法,网格工作负荷由模型得到,并用一段工作负荷的平均响应时间衡量调度性能.实验结果表明,即使在作业提交点分布不均匀且运行时间估计不准确情况下,爬山法仍可有效改善单地点作业的调度.     

An efficient grid scheduling strategy for data parallel applications

Scheduling large-scale application in heterogeneous grid systems is a fundamental NP-complete problem that is critical to obtain good performance and execution cost. To achieve high performance in a grid system it requires effective task partitioning, resource management and load balancing. The heterogeneous and dynamic nature of a grid, as well as the diverse demands of applications running on the grid, makes grid scheduling a major task. Existing schedulers in wide-area heterogeneous systems require a large amount of information about the application and the grid environment to produce reasonable schedules. However, this required information may not be available, may be too expensive to collect, or may increase the runtime overhead of the scheduler such that the scheduler is rendered ineffective. We believe that no one scheduler is appropriate for all grid systems and applications. This is because while data parallel applications in which further data partitioning is possible can be further improved by efficient management of resources, smart selection of resources and load balancing can be possible, in functional/not-dividable-task parallel applications such partitioning is either not possible or difficult or expensive in term of performance. In this paper, we propose a scheduler for data parallel applications (SDPA) which offers an efficient task partitioning and load balancing strategy for data parallel applications in grid environment. The proposed SDPA offers two major features: maintaining job priority even if insufficient number of free resources is available and pre-task assignment to cut the idle time of nodes. The SDPA selects nodes smartly according to the nature of task and the nodes’ resources availability. Simulation results conducted reveal that SDPA achieves performance improvement over reported strategies in the reviewed literature in terms of execution time, throughput and waiting time.     

基于组合双向拍卖的云资源调度方法

针对跨数据中心的资源调度问题，提出了一种基于组合双向拍卖（PCDA）的资源调度方案。首先，将云资源拍卖分为三个部分：云用户代理报价、云资源提供商要价、拍卖代理组织拍卖；其次，在定义用户的优先级及任务紧迫度的基础上，在拍卖过程中估算每一个工作发生的服务等级协议（SLA）违规并以此计算云提供商的收益，同时每轮竞拍允许成交多项交易；最终达到根据用户等级合理分配云资源调度的效果。仿真实验结果表明该算法保证了竞拍成功率，与传统一次拍卖成交一项的组合双向拍卖方案相比，PCDA在竞拍时间段产生的能耗降低了35.00%，拍卖云提供商的利润提高了约38.84%。     

A fault-tolerant scheduling system for computational grids

Fault-tolerant scheduling is an important issue for computational grid systems, as grids typically consist of strongly varying and geographically distributed resources. The main scheduling strategy of most fault-tolerant scheduling systems depends on the response time and fault index when selecting a resource to execute a certain job.In this paper, a scheduling system is presented that depends on a new factor called scheduling indicator in selecting resources. This factor comprises of the response time and the failure rate of grid resources. Whenever a grid scheduler has jobs to schedule on grid resources, it uses the scheduling indicator to generate the scheduling decisions. The main scheduling strategy of the system is to select resources that have the lowest tendency to fail. Extensive simulation experiments are conducted to quantify the performance of the proposed system. Experiments have shown that the proposed system can considerably improve grid performance in terms of throughput, unavailability, turnaround time, and fail tendency.     

Grid performance prediction using state‐space model

One of the main challenges of scheduling algorithms in Grid environment is the autonomy of sites, which makes it difficult for the grid scheduler to estimate the exact cost of a task execution on different sites. In this paper, we present a solution for this problem based on data history (workload traces) and time series techniques. The main focus of this work is devoted to forecasting the task waiting time in a resource queue, which is under the control of a local scheduler with distinctive scheduling policy. The main contribution of this work is the consideration of a special property of the grid resources, the dynamic membership, i.e. a resource may exit and then come back to the grid environment repeatedly. When the resource belongs to the grid environment, its workload trace (log file) is considered as a correct log. On the other hand, when the resource leaves the grid, the log file during this period is considered as a defective part of the trace. As the defective parts contain some useful information, after repairing these defective parts, they can be used for forecasting purposes. Of this, we employ state‐space model along with the associated Kalman recursions in conjunction with the Expectation‐Maximization algorithm to repair the defective waiting time series such as a correct log file by which the resource seems never to have left the grid. The experimental results on a number of workload logs demonstrated that this approach can achieve an average prediction error, between 22 and 64% less than those incurred by other rival methods. Copyright © 2009 John Wiley & Sons, Ltd.