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

机译:基于大规模云数据中心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已经活跃,HPC和大规模数据中心的使用频率正在增加数据处理。此外,在云计算环境中,正在构建由巨大节点组成的大规模环境,并且由于大规模的节点配置而导致的额外问题。在本文中,我们每次在由大型节点组成的云环境中迁移虚拟机(VM),分析大型节点的收集状态信息的开销。然后,我们介绍动态缓存刷新(DCR)方案,以减轻大规模节点的状态信息的不必要的时间。我们的DCR方案能够在必要时获得最新信息,并在没有频繁缓存刷新的情况下降低通信开销。基于实验性能评估,我们证明我们的动态缓存刷新(DCR)方案实现了与现有云调度程序相比的网络瓶颈和不必要的延迟时间的显着减少。

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