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Load Balancing in Grid Computing Using Ant Colony Algorithm and Max-min Technique

机译:使用蚁群算法和最大最小技术的网格计算中的负载均衡

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Stagnation is one of the complicated issues in Grid computing systems, which is caused by random arrival of tasks and heterogeneous resources. Stagnation occurs when a large number of submitted tasks are assigned to a specific resource and make it overflow. To prevent this scenario, a load balancing algorithm based on Ant Colony algorithm and Max-min technique is proposed in this paper. In the proposed algorithm, the resource manager of the system finds the best resource for a submitted task according to a matrix that indicates the characteristics of all resources as pheromone values. By choosing the best resource for the submitted task, a local pheromone update is applied to the selected one to reduce the tendency of being selected by onward new tasks. After this assigned task is executed properly, a global pheromone update is performed to renew the status of all resources for the next submitted tasks. To avoid stagnation, a comparison between a predefined threshold and the pheromone value of each resource is performed to keep the number of assigned tasks below this threshold. Due to harmonizing the resources?characteristics and tasks, the proposed algorithm is able to reduce the response time of the submitted tasks while it is simple to be implemented.
机译:停滞是网格计算系统中的复杂问题之一,它是由任务的随机到达和异构资源引起的。当将大量提交的任务分配给特定资源并使其溢出时,就会发生停滞。为了避免这种情况的发生,本文提出了一种基于蚁群算法和Max-min技术的负载均衡算法。在提出的算法中,系统的资源管理器根据将所有资源的特征表示为信息素值的矩阵,为提交的任务找到最佳资源。通过为提交的任务选择最佳资源,将本地信息素更新应用于所选的信息素,以减少被新任务转发选择的趋势。正确执行此分配的任务后,将执行全局信息素更新,以更新下一个提交任务的所有资源的状态。为了避免停滞,在预定义阈值和每个资源的信息素值之间进行比较,以使分配的任务数保持在此阈值以下。由于统一资源,特征和任务,该算法能够减少提交任务的响应时间,并且易于实现。

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