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Resource Utilization-Aware Scheduling Technique Based on Dynamic Cache Refresh Scheme in Large-Scale Cloud Data centers

机译:大型云数据中心基于动态缓存刷新方案的资源利用感知调度技术

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In recent years, as researches on big data processing have become active, the frequency of use of HPC and large-scale data center is increasing for data processing. In addition, in the cloud computing environments, a large-scale environment consisting of huge nodes is being built, and additional problems arise due to the large-scale node configuration. In this paper, we analyze the overhead of gathering status information of large-scale nodes each time a virtual machine (VM) is migrated in a cloud environment composed of large-scale nodes. Then, we present a dynamic cache refresh (DCR) scheme to mitigate the unnecessary time of gathering status information of large-scale nodes. Our DCR scheme is able to obtain up-to-date information when necessary and reduce communication overhead without frequent cache refreshes. Based on experimental performance evaluations, we prove that our dynamic cache refresh (DCR) scheme achieves a significant reduction of the network bottleneck and the unnecessary delay time compared to existing cloud scheduler.
机译:近年来,随着对大数据处理的研究变得活跃,HPC和大型数据中心在数据处理中的使用频率正在增加。另外,在云计算环境中,正在构建由巨大节点组成的大规模环境,并且由于大规模节点配置而引起了其他问题。在本文中,我们分析了每次在由大型节点组成的云环境中迁移虚拟机(VM)时收集大型节点状态信息的开销。然后,我们提出了一种动态缓存刷新(DCR)方案,以减少不必要的时间来收集大规模节点的状态信息。我们的DCR方案能够在必要时获取最新信息,并减少通信开销,而无需频繁刷新缓存。根据实验性能评估,我们证明了与现有的云调度程序相比,我们的动态缓存刷新(DCR)方案可显着减少网络瓶颈并减少不必要的延迟时间。

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