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A probabilistic task scheduling method for grid environments

机译：网格环境下的概率任务调度方法

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This paper presents a probabilistic task scheduling method to minimize the overall mean response time of the tasks submitted to the grid computing environments. Minimum mean response time of a given task can be obtained by finding a subset of appropriate computational resources to service the task. To achieve this, a discrete time Markov chain (DTMC) representing the task scheduling process within the grid environment is constructed. The connection probabilities between the nodes representing the grid managers and resources can be considered as transition probabilities of the obtained DTMC. Knowing the mean response times of the managers and resources, and finding fundamental matrix of the DTMC, the mean response time related to each of the absorbing DTMCs existing inside the overall DTMC can be computed. Minimizing the obtained mean response times and taking into account the probability constraints in each of the absorbing DTMCs, a nonlinear programming (NLP) problem is defined. Solving the NLP problem, the connection probabilities between the managers and resources are obtained. Finally, using the connection probabilities, the best scheduling path within the environment and the minimum mean response time of a particular task can be achieved. In a case in which there is only one optimal scheduling choice within the environment, the proposed method can deterministically find such scheduling by assigning zero or one to the connection probabilities. Results obtained from evaluating the proposed method on the hypothesis and real grid environments show the preference of the proposed method compared to the other methods in minimizing both the overall mean response time of the tasks and total makespan of the environment.
机译：本文提出了一种概率任务调度方法，以最小化提交给网格计算环境的任务的总体平均响应时间。给定任务的最小平均响应时间可以通过找到服务任务的适当计算资源的子集获得。为此，构建了代表网格环境中任务调度过程的离散时间马尔可夫链（DTMC）。代表网格管理器的节点与资源之间的连接概率可以视为获得的DTMC的转移概率。知道管理者和资源的平均响应时间，找到DTMC的基本矩阵，就可以计算出与整个DTMC内部存在的每个吸收DTMC相关的平均响应时间。最小化获得的平均响应时间并考虑每个吸收式DTMC中的概率约束，定义了非线性编程（NLP）问题。解决了自然语言处理问题，获得了管理者与资源之间的连接概率。最后，使用连接概率，可以实现环境中最佳的调度路径和特定任务的最小平均响应时间。在环境中只有一个最佳调度选择的情况下，所提出的方法可以通过为连接概率分配零或一来确定性地找到这种调度。通过在假设和真实网格环境下评估该提议的方法而获得的结果表明，相比于其他方法，该提议的方法在最小化任务的总体平均响应时间和环境的总寿命方面具有优势。

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