Monte Carlo Based Server Consolidation for Energy Efficient Cloud Data Centers
Monte Carlo Based Server Consolidation for Energy Efficient Cloud Data Centers
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DOI：
10.1109/cloudcom.2019.00046
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发表时间：
2019-12
期刊：
影响因子：
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通讯作者：
B. Harris;Nihat Altiparmak
中科院分区：
文献类型：
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作者：
B. Harris;Nihat Altiparmak
数据中心不断增长的能源消耗是一个引人注目的全球性问题，有效的服务器整合是节能云数据中心的核心。装箱的一个变体可以用来模拟服务器整合问题，其中的约束是多维和异构向量，而不是标量，目标是使用最少数量的物理服务器来满足所请求的资源分配。由于装箱是NP难的，我们依靠物流来获得实际的解决方案。首先拟合递减（FFD）的基础上的算法的变化已被证明是有效的，在理论和实践中的一维均匀的情况下。然而，多维和异构方面的服务器整合问题，使其更加复杂，需要额外的研究，以适应FFD的服务器整合问题。在本文中，我们提出了一种新的基于FFD的服务器整合技术，使用蒙特卡罗方法和香农熵，它考虑了资源瓶颈，并动态调整方差在不同的资源利用率。所提出的启发式优于现有的技术在所有情况下，实现平均2-5%的最优的中到高的资源利用率的变化，并在10%内比最优的平均所有情况下。
The growing energy consumption of data centers is a compelling global problem and effective server consolidation is at the heart of energy efficient cloud data centers. A variant of bin packing can be used to model the server consolidation problem, where the constraints are multidimensional and heterogeneous vectors rather than scalars and the goal is to satisfy the requested resource allocation using the minimum number physical servers. Since bin packing is NP-hard, we rely on heuristics for practical solutions. Variations of First Fit Decreasing (FFD) based heuristics have been shown to be effective both in theory and practice for the one dimensional homogeneous case. However, the multidimensional and heterogeneous aspects of the server consolidation problem make it more complicated, requiring additional research to adapt FFD to the server consolidation problem. In this paper, we present a new FFD-based server consolidation technique using a Monte Carlo method and Shannon entropy, which considers resource bottlenecks and dynamically adjusts to variance in the utilization of different resources. The proposed heuristic outperforms existing techniques in all scenarios, achieving within 2-5% of optimal on average for medium to high variance in resource utilization, and within 10% worse than optimal on average for all scenarios.