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Power efficient server consolidation for Cloud data center

机译:云数据中心的高效服务器整合

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Cloud computing has become an essential part of the global digital economy due to its extensibility, flexibility and reduced costs of operations. Nowadays, data centers (DCs) contain thousands of different machines running a huge number of diverse applications over an extended period. Resource management in Cloud is an open issue since an efficient resource allocation can reduce the infrastructure running cost. In this paper, we propose a snapshot-based solution for server consolidation problem from Cloud infrastructure provider (CIP) perspective. Our proposed mathematical formulation aims at reducing power cost by employing efficient server consolidation, and also considering the issues such as (i) mapping incoming and failing virtual machines (VMs), (ii) reducing a total number of VM migrations and (iii) consolidating running server workloads. We also compare the performance of our proposed model to the well-known Best Fit heuristics and its extension to include server consolidation via VM migration denoted as Best Fit with Consolidation (BFC). Our proposed mathematical formulation allows us to measure the solution quality in absolute terms, and it can also be applicable in practice. In our simulations, we show that relevant improvements (from 6% to 15%) over the widely adopted Best Fit algorithm achieved in a reasonable computing time.
机译:由于云计算的可扩展性,灵活性和降低的运营成本,它已成为全球数字经济的重要组成部分。如今,数据中心(DC)包含成千上万台不同的计算机,这些计算机在很长的一段时间内运行着大量不同的应用程序。云中的资源管理是一个未解决的问题,因为有效的资源分配可以降低基础架构的运行成本。在本文中,我们从云基础架构提供商(CIP)的角度为服务器整合问题提出了一种基于快照的解决方案。我们提出的数学公式旨在通过采用有效的服务器整合来降低功耗,并考虑以下问题:(i)映射传入和失败的虚拟机(VM),(ii)减少VM迁移的总数以及(iii)合并运行服务器工作负载。我们还将我们提出的模型的性能与著名的“最佳拟合”启发式方法及其扩展进行了比较,将其扩展到包括通过VM迁移进行的服务器整合,称为“最佳整合”(BFC)。我们提出的数学公式使我们能够绝对地衡量解决方案的质量,并且也可以在实践中应用。在我们的仿真中,我们表明,在合理的计算时间内,与广泛采用的Best Fit算法相比,有相关的改进(从6%到15%)。

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