利用洞察式虚拟机放置降低私有云的前期成本
Reducing the Upfront Cost of Private Clouds with Clairvoyant Virtual Machine Placement
摘要：
虽然公共云仍然占据了整个云基础设施的最大部分，但私有云由于其更好的安全性和隐私控制，正吸引着越来越多的行业和学术界的兴趣。根据现有的研究，高昂的前期成本是与私有云相关的最关键挑战之一。为了降低成本和提高性能，虚拟机放置（VMP）方法已被广泛研究，然而，这些方法很少关注私有云。本文提出了一种异构和多维透视动态装箱（CDBP）模型，调度器可以使用虚拟机到达时间和持续时间的附加信息进行更有效的VMP流程，以减少数据中心规模，从而降低私有云的前期成本。此外，提出了一种新的分支定界算法与分治策略（DCBB），以有效地和高效地处理派生的问题。一个国家的最先进的和几个经典的VMP方法也被修改，以适应所提出的模型，以观察他们的性能，并与我们提出的算法进行比较。在真实世界和合成工作负载上进行了大量的实验，以评估算法的准确性和效率。实验结果表明，DCBB提供了接近最优的解决方案，收敛速度比其他基于搜索的算法评估快得多。特别是，DCBB产生的最佳解决方案，为现实世界的工作负载的执行时间是一个数量级短于所需的原始分支定界（BB）算法。
英文摘要：
Although public clouds still occupy the largest portion of the total cloud infrastructure, private clouds are attracting increasing interest from both industry and academia because of their better security and privacy control. According to the existing studies, the high upfront cost is among the most critical challenges associated with private clouds. To reduce cost and improve performance, virtual machine placement (VMP) methods have been extensively investigated, however, few of these methods have focused on private clouds. This paper proposes a heterogeneous and multidimensional clairvoyant dynamic bin packing (CDBP) model, in which the scheduler can conduct more efficient VMP processes using additional information on the arrival time and duration of virtual machines to reduce the datacenter scale and thereby decrease the upfront cost of private clouds. In addition, a novel branch-and-bound algorithm with a divide-and-conquer strategy (DCBB) is proposed to effectively and efficiently handle the derived problem. One state-of-the-art and several classic VMP methods are also modified to adapt to the proposed model to observe their performance and compare with our proposed algorithm. Extensive experiments are conducted on both real-world and synthetic workloads to evaluate the accuracy and efficiency of the algorithms. The experimental results demonstrate that DCBB delivers near-optimal solutions with a convergence rate that is much faster than those of the other search-based algorithms evaluated. In particular, DCBB yields the optimal solution for a real-world workload with an execution time that is an order of magnitude shorter than that required by the original branch-and-bound (BB) algorithm.
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