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An Efficient and Energy-Aware Cloud Consolidation Algorithm for Multimedia Big Data Applications

机译:多媒体大数据应用的高效节能能源云整合算法

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It is well known that cloud computing has many potential advantages over traditional distributed systems. Many enterprises can build their own private cloud with open source infrastructure as a service (IaaS) frameworks. Since enterprise applications and data are migrating to private cloud, the performance of cloud computing environments is of utmost importance for both cloud providers and users. To improve the performance, previous studies on cloud consolidation have been focused on live migration of virtual machines based on resource utilization. However, the approaches are not suitable for multimedia big data applications. In this paper, we reveal the performance bottleneck of multimedia big data applications in cloud computing environments and propose a cloud consolidation algorithm that considers application types. We show that our consolidation algorithm outperforms previous approaches.
机译:众所周知,与传统的分布式系统相比,云计算具有许多潜在的优势。许多企业可以使用开源基础架构即服务(IaaS)框架构建自己的私有云。由于企业应用程序和数据正在迁移到私有云,因此云计算环境的性能对于云提供商和用户都至关重要。为了提高性能,以前有关云整合的研究一直集中在基于资源利用率的虚拟机实时迁移上。但是,这些方法不适用于多媒体大数据应用。在本文中,我们揭示了云计算环境中多媒体大数据应用程序的性能瓶颈,并提出了一种考虑应用程序类型的云合并算法。我们证明了我们的合并算法优于以前的方法。

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