异构计算环境中启发式任务调度方法
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摘要
异构计算环境由各种具有不同计算能力的资源构成,用以满足计算密集型应用的需要.这些应用在计算上具有不同的需求和约束.异构计算系统和计算网格使用一组地理上分布的资源来解决困难的计算问题.异构计算系统中的一个主要的挑战是如何有效地利用可用的资源.在这类系统中,系统资源被多个应用所共享,用户提出的应用具有特定的服务质量需求.一种高效利用异构计算系统或计算网格的方法是根据计算需求将应用分解为若干任务,不同的任务会适合于运行在不同的机器上.一旦应用被分解为任务,每个任务需要被分配到合适的机器上执行,从而最优化某个特定的目标函数.在异构系统中,将任务分配到机器的问题被证明是NP完全问题.因此,亟需设计启发式技术来获得近似最优解.本文主要研究异构计算系统和计算网格中任务的分配问题.
为解决异构计算系统和计算网格中的任务调度问题,我们提出多种调度算法.我们提出了一种新的任务调度启发式方法,即高方差优先算法(HSTDF).该方法将任务的期望执行时间的方差作为选择准则.任务的期望执行时间的方差表示了该任务在不同机器上执行时间的差异量.我们的方法是将具有最高方差的任务首先进行调度.直观上,具有较低方差的任务在不同机器上的执行时间相差较小,因此将这些任务延迟调度对整体完成时间的影响较小.相反,具有较高方差的任务在不同机器上的执行时间相差较大,延迟调度这些任务可能会妨碍这些任务被分配到速度更快的机器,因为这些机器可能己经被之前调度过的任务占据了,这种情况将导致任务整体完成时间的增加.因此,具有最高方差的任务优先进行调度.我们进行了大量的实验来检验该启发式方法在各种情况下的有效性.实验结果表明该方法优于现有的方法.因此,将任务的期望执行时间的方差作为选择准则有助于提高调度的效率.
此外,我们还提出了一种新的网格计算中的服务质量导向的任务调度算法.服务质量在不同的应用背景下具有不同的含义.例如,网络中的服务质量表示应用所期望的带宽. CPU的服务质量表示所需要的速度,如FLOPS,或对潜在CPU性能的利用.我们的研究考虑一种一维服务质量.我们将网络的服务质量表示为它的带宽.在现有的网格任务调度中,具有不同级别服务质量要求的任务会请求不同的资源.没有服务质量要求的任务可以在高服务质量的资源上执行也可在低服务质量的资源上执行.然而,带有高服务质量请求的任务只能在高服务质量资源上执行.因此,让低服务质量任务占据高服务质量资源,同时让高服务质量要求的任务等待是不可行的,因为低服务质量资源仍然空闲.为克服这些不足,我们提出了一种新的调度算法.该算法将服务质量匹配考虑进任务调度决策中.
为了在异构计算系统中进行任务调度,人们提出了大量启发式方法.每种启发式方法都建立在不同的假设基础上,从而给出近似最优解,然而对于一组特定的任务,选择哪种方法进行调度却没有被研究过.为解决这个问题,我们对多种启发式方法进行了大量实验研究,并考察哪个方法能够给出最小的任务完成时间.我们实现、分析、并系统地比较了16种贪心式启发方法,其中7种是新提出的方法.比较实验所使用的数据模型由基于coefficient-of-variation的模型实现.大量的模拟实验指出了在某种贪心启发方法优于其他15种方法的情况.
Heterogeneous computing (HC) environment is composed of various resources with different computational capabilities to meet the demands of computing-intensive applications that have diverse computational requirements and constraints. In HC systems and computational grids, a diverse set of geographically distributed resources are used to solve challenging problems. A major challenge in using these systems is to effectively use available resources. System resources are shared among applications. Applications are submitted from various users with specific QoS requirements. One way to take advantage of HC or computational grids is to decompose an application into several tasks based on the computational requirements. Different tasks may be best suited for different machines. Once the application is decomposed into tasks, each task needs to be assigned to suitable machine in order to optimize a given objective function. The problem of assigning application tasks to the machines in a HC environment has been shown, in general, to be NP-complete. Therefore the development of heuristic techniques to find near optimal solution is required. The focus of this dissertation is the assignment of application tasks onto HC systems and computational grids.
We developed several scheduling algorithms to address the problem of producing efficient schedules in HC environment and computational grids. We propose a new task scheduling heuristic, high standard deviation first (HSTDF), which considers the standard deviation of expected execution time of a task as a selection criterion. Standard deviation of expected execution time of task represents the amount of variation in task execution time on different machines: Our conclusion is tasks having high standard deviation must be assigned first for scheduling. Intuitively, tasks having low standard deviation of task execution time have less variation in execution time on different machines and hence, their delayed assignment for scheduling will not affect overall makespan much. Moreover, the tasks with higher standard deviation of task execution time exhibit more variation in their execution time on different machines. A delayed assignment of such tasks might hinder their chances of occupying faster machines as some other tasks might occupy these machines earlier. Such a scenario would result in an increase in the system makespan, so the tasks having high standard deviation must be assigned first. Large numbers of experiments were carried out to check the effectiveness of proposed heuristic in different scenarios, which clearly revealed that proposed heuristic outperformed existing heuristics in terms of average makespan and hence provided strong evidence that using standard deviation of execution time as selection criterion improved task scheduling.
Next, in this thesis we introduce a novel QoS guided task scheduling algorithm for Grid computing. The term quality of service (QoS) is used differently based on its context while applying it to a resource. For instance, QoS for a network may mean the desirable bandwidth for the application; QoS for CPU may mean the requested speed, like FLOPS, or the utilization of the underlying CPU. In our study, a one dimension QoS is considered. We represent the QoS of a network by its bandwidth. In current Grid task scheduling, tasks with different levels of QoS requests compete for resources. While a task with no QoS request can be executed on both high QoS and low QoS resources, a task that requests a high QoS service can only be executed on a resource providing high quality of service. Thus, it is possible for low QoS tasks to occupy high QoS resources while high QoS tasks wait as low QoS resources remain idle. To overcome this shortcoming, we present the new task scheduling algorithm for computational grids which take the QoS matching into consideration while taking the scheduling decision.
A plethora of heuristics has been proposed for assignment of tasks to machines in HC environment. Each heuristic has different underlying assumptions to produce near optimal solution however no work reports which heuristic should be used for a given set of tasks to be executed on different machines. To solve this dilemma, we conduct a performance evaluation of task scheduling heuristics in HC environment and find out the task assignment strategy that gives the minimum makespan. A collection of 16 greedy heuristics, including 7 newly proposed ones, are implemented, analyzed, and systematically compared within a uniform model of task execution time. This model is implemented by the coefficient-of-variation based method. Extensive simulations illustrate the circumstances when a specific greedy heuristic would outperform the other 15 greedy heuristics.
为解决异构计算系统和计算网格中的任务调度问题,我们提出多种调度算法.我们提出了一种新的任务调度启发式方法,即高方差优先算法(HSTDF).该方法将任务的期望执行时间的方差作为选择准则.任务的期望执行时间的方差表示了该任务在不同机器上执行时间的差异量.我们的方法是将具有最高方差的任务首先进行调度.直观上,具有较低方差的任务在不同机器上的执行时间相差较小,因此将这些任务延迟调度对整体完成时间的影响较小.相反,具有较高方差的任务在不同机器上的执行时间相差较大,延迟调度这些任务可能会妨碍这些任务被分配到速度更快的机器,因为这些机器可能己经被之前调度过的任务占据了,这种情况将导致任务整体完成时间的增加.因此,具有最高方差的任务优先进行调度.我们进行了大量的实验来检验该启发式方法在各种情况下的有效性.实验结果表明该方法优于现有的方法.因此,将任务的期望执行时间的方差作为选择准则有助于提高调度的效率.
此外,我们还提出了一种新的网格计算中的服务质量导向的任务调度算法.服务质量在不同的应用背景下具有不同的含义.例如,网络中的服务质量表示应用所期望的带宽. CPU的服务质量表示所需要的速度,如FLOPS,或对潜在CPU性能的利用.我们的研究考虑一种一维服务质量.我们将网络的服务质量表示为它的带宽.在现有的网格任务调度中,具有不同级别服务质量要求的任务会请求不同的资源.没有服务质量要求的任务可以在高服务质量的资源上执行也可在低服务质量的资源上执行.然而,带有高服务质量请求的任务只能在高服务质量资源上执行.因此,让低服务质量任务占据高服务质量资源,同时让高服务质量要求的任务等待是不可行的,因为低服务质量资源仍然空闲.为克服这些不足,我们提出了一种新的调度算法.该算法将服务质量匹配考虑进任务调度决策中.
为了在异构计算系统中进行任务调度,人们提出了大量启发式方法.每种启发式方法都建立在不同的假设基础上,从而给出近似最优解,然而对于一组特定的任务,选择哪种方法进行调度却没有被研究过.为解决这个问题,我们对多种启发式方法进行了大量实验研究,并考察哪个方法能够给出最小的任务完成时间.我们实现、分析、并系统地比较了16种贪心式启发方法,其中7种是新提出的方法.比较实验所使用的数据模型由基于coefficient-of-variation的模型实现.大量的模拟实验指出了在某种贪心启发方法优于其他15种方法的情况.
Heterogeneous computing (HC) environment is composed of various resources with different computational capabilities to meet the demands of computing-intensive applications that have diverse computational requirements and constraints. In HC systems and computational grids, a diverse set of geographically distributed resources are used to solve challenging problems. A major challenge in using these systems is to effectively use available resources. System resources are shared among applications. Applications are submitted from various users with specific QoS requirements. One way to take advantage of HC or computational grids is to decompose an application into several tasks based on the computational requirements. Different tasks may be best suited for different machines. Once the application is decomposed into tasks, each task needs to be assigned to suitable machine in order to optimize a given objective function. The problem of assigning application tasks to the machines in a HC environment has been shown, in general, to be NP-complete. Therefore the development of heuristic techniques to find near optimal solution is required. The focus of this dissertation is the assignment of application tasks onto HC systems and computational grids.
We developed several scheduling algorithms to address the problem of producing efficient schedules in HC environment and computational grids. We propose a new task scheduling heuristic, high standard deviation first (HSTDF), which considers the standard deviation of expected execution time of a task as a selection criterion. Standard deviation of expected execution time of task represents the amount of variation in task execution time on different machines: Our conclusion is tasks having high standard deviation must be assigned first for scheduling. Intuitively, tasks having low standard deviation of task execution time have less variation in execution time on different machines and hence, their delayed assignment for scheduling will not affect overall makespan much. Moreover, the tasks with higher standard deviation of task execution time exhibit more variation in their execution time on different machines. A delayed assignment of such tasks might hinder their chances of occupying faster machines as some other tasks might occupy these machines earlier. Such a scenario would result in an increase in the system makespan, so the tasks having high standard deviation must be assigned first. Large numbers of experiments were carried out to check the effectiveness of proposed heuristic in different scenarios, which clearly revealed that proposed heuristic outperformed existing heuristics in terms of average makespan and hence provided strong evidence that using standard deviation of execution time as selection criterion improved task scheduling.
Next, in this thesis we introduce a novel QoS guided task scheduling algorithm for Grid computing. The term quality of service (QoS) is used differently based on its context while applying it to a resource. For instance, QoS for a network may mean the desirable bandwidth for the application; QoS for CPU may mean the requested speed, like FLOPS, or the utilization of the underlying CPU. In our study, a one dimension QoS is considered. We represent the QoS of a network by its bandwidth. In current Grid task scheduling, tasks with different levels of QoS requests compete for resources. While a task with no QoS request can be executed on both high QoS and low QoS resources, a task that requests a high QoS service can only be executed on a resource providing high quality of service. Thus, it is possible for low QoS tasks to occupy high QoS resources while high QoS tasks wait as low QoS resources remain idle. To overcome this shortcoming, we present the new task scheduling algorithm for computational grids which take the QoS matching into consideration while taking the scheduling decision.
A plethora of heuristics has been proposed for assignment of tasks to machines in HC environment. Each heuristic has different underlying assumptions to produce near optimal solution however no work reports which heuristic should be used for a given set of tasks to be executed on different machines. To solve this dilemma, we conduct a performance evaluation of task scheduling heuristics in HC environment and find out the task assignment strategy that gives the minimum makespan. A collection of 16 greedy heuristics, including 7 newly proposed ones, are implemented, analyzed, and systematically compared within a uniform model of task execution time. This model is implemented by the coefficient-of-variation based method. Extensive simulations illustrate the circumstances when a specific greedy heuristic would outperform the other 15 greedy heuristics.
引文
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