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Minimizing Data Access Latencies via Virtual Machine Placement Method in Datacenter

机译:通过Datacenter中的虚拟机放置方法最小化数据访问延迟

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The processing framework of large-scale data is becoming a major concern due to an explosive growth of data intensive applications in the cloud environment, such as MapReduce/Hadoop architecture. Many virtual machines (VMs) are used for processing large-scale data of cloud applications. Therefore, the total completion time of a task is an important index to evaluate the cloud performance. The access latency between nodes is one of the key factors affecting the task completion time for computing-intensive applications. Additionally, minimizing total access time can reduce the overall bandwidth cost of running the job. This paper proposes an optimization model focused on optimizing VMs placement so as to minimize the total data access latency where the data sets have been located. According to the proposed model, our VMs optimization problem is linear programming. Therefore, we obtain the optimum solution of our model by the branch-andbound algorithm that its time complexity is O(2NM) (where N is the number of the data nodes and M is the number of VMs). Simultaneously, we also present a greedy algorithm, which has O(NM) of time complexity, to solve our model. Finally, the simulation results show that all of the solutions of our model are superior to existing models and close to the optimal value.
机译:大规模数据的处理框架由于云环境中的数据密集型应用程序的爆炸性增长而成为一个主要问题,例如MapReduce / Hadoop架构。许多虚拟机(VM)用于处理云应用的大规模数据。因此,任务的总完成时间是评估云性能的重要索引。节点之间的访问延迟是影响计算机密集型应用程序任务完成时间的关键因素之一。此外,最小化总访问时间可以降低运行作业的整体带宽成本。本文提出了一种优化模型,专注于优化VMS放置,以最小化数据集所在的总数据访问延迟。根据拟议的模型,我们的VMS优化问题是线性编程。因此,我们通过分支算法获得模型的最佳解决方案,即其时间复杂度是O(2 nm )(其中n是数据节点的数量,m是VM的数量)。同时,我们还提出了一种贪婪的算法,它具有时间复杂度的O(nm),以解决我们的模型。最后,仿真结果表明,我们模型的所有解决方案都优于现有模型,并靠近最佳值。

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