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Data Intensive, Computing and Network Aware (DCN) Cloud VMs Scheduling Algorithm

机译：数据密集型，计算和网络感知（DCN）云VMS调度算法

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The emergence of cloud computing technology aims at sharing resources such as storage, knowledge, computation and information for scientific research at an expanded scale. The application about associated data are deployed by the cloud users as paying the bills when they get due. Such data-intensive applications are normally commanded by the virtual machines (VMs). Data at a large scale are analyzed by data intensive applications and their replications are made for diffusing them among various geographical sites. In case the very spot of execution of a job gets no data replication, then data are streamed from a distant site. The overall execution of the job will deteriorate with such data transfer from remote sites. The decisive factors in the performance of these applications are workload volume, network status between storage nodes SNs and CNs, workload types I/O computation or I/O data-intensive and CPU attributes into computing node CN. Thus, the completion time differs according to the application jobs in workload on the basis of retrieval of vast data and decision of VM placement. Our proposal for obtaining elevated performance in the completion time of overall jobs along with alleviating the throughput of cloud links is VMs placement that takes both the I/O data and computation resources into consideration. This algorithm tries to diminish the completion time of overall jobs (including both time for data transfer and computing time). The CloudSim Simulator results show that our algorithm with the ability of significantly increasing and decreasing the performance of overall performance and the completion time of average jobs respectively instead of earlier proposal for VMs placement in literature review.
机译：云计算技术的出现旨在以扩展规模分享资源，例如科学研究的存储，知识，计算和信息。关于关联数据的应用程序由云用户部署，因为在截止到期时支付账单。这种数据密集型应用程序通常由虚拟机（VM）命令。通过数据密集型应用分析大规模的数据，并使它们的复制在各种地理位置中扩散。在作业的执行点的情况下，没有数据复制，则从远程站点流式流。作业的整体执行将使远程站点的数据传输恶化。这些应用程序性能的决定性因素是工作负载量，存储节点SNS和CNS之间的网络状态，工作负载类型I / O计算或I / O数据密集型和CPU属性到计算节点CN。因此，完成时间根据工作负载中的应用作业基于检索庞大数据和VM放置的决策而不同。我们在整体作业的完井时间内获得升高性能的提案以及缓解云链路的吞吐量是VMS放置，它考虑了I / O数据和计算资源。该算法试图减少整个作业的完成时间（包括数据传输和计算时间的时间）。 CloudSIM模拟器结果表明，我们的算法具有显着增加和降低整体性能的性能和平均工作的完成时间的能力，而不是在文献审查中的VMS放置的早期提案。

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