[论文解读] Heterogeneous and Multidimensional Clairvoyant Dynamic Bin Packing for Virtual Machine Placement.

Yan Zhao, Zhan Zhang+3 more|arXiv (Cornell University)|Feb 9, 2018

Cloud Computing and Resource Management参考文献 25被引用 3

一句话总结

本文提出了一种异构的、多维的全知动态装箱模型（CDBP），用于私有云中的虚拟机部署，以降低前期基础设施成本。通过利用时间感知调度和一种新颖的分治分支定界（DCBB）算法，该方法在显著更快的收敛速度下实现了最优的服务器数量减少，优于传统分支定界算法，在合成与真实工作负载上均优于改进的首次适应和蚁群算法。

ABSTRACT

Although the public cloud still occupies the largest portion of the total cloud infrastructure, the private cloud is attracting increasing interest because of its better security and privacy control. According to previous research, a high upfront cost is among the most serious challenges associated with private cloud computing. Virtual machine placement (VMP) is a critical operation for cloud computing, as it improves performance and reduces cost. Extensive VMP methods have been researched, but few have been designed to reduce the upfront cost of private clouds. To fill this gap, in this paper, a heterogeneous and multidimensional clairvoyant dynamic bin packing (CDBP) model, in which the scheduler can conduct more efficient scheduling with additional time information to reduce the size of the datacenter and, thereby, decrease the upfront cost, is applied. An innovative branch-and-bound algorithm with the divide-and-conquer strategy (DCBB) is proposed to reduce the number of servers (#servers), with fast processing speed. In addition, some algorithms based on first fit (FF) and the ant colony system (ACS) are modified to apply them to the CDBP model. Experiments are conducted on generated and real-world data to check the performance and efficiency of the algorithms. The results confirm that the DCBB can make a tradeoff between performance and efficiency and also achieves a much faster convergence speed than that of other search-based algorithms. Furthermore, the DCBB yields the optimal solution under real-world workloads in much less runtime (by an order of magnitude) than required by the original branch-and-bound (BB) algorithm.

研究动机与目标

• 通过优化虚拟机部署来应对私有云部署中的高前期成本挑战。
• 通过智能的时间感知虚拟机调度，减少数据中心规模和基础设施成本。
• 开发一种可扩展且高效的算法，在动态虚拟机部署中实现性能与运行时效率的平衡。
• 实现在异构、多维环境（CPU、内存、存储、网络）下的最优服务器利用率。

提出的方法

• 构建一种异构的、多维的全知动态装箱模型（CDBP），整合未来虚拟机的到达与离开时间。
• 提出一种新颖的分治分支定界（DCBB）算法，以加速收敛并提升运行时效率。
• 将首次适应（FF）和蚁群系统（ACS）算法适配至CDBP模型，用于对比评估。
• 采用分治策略对搜索空间进行划分，降低精确分支定界算法的计算负担。
• 集成时间感知调度，以预测虚拟机生命周期并最小化资源碎片化。
• 采用递归分解方法，比标准分支定界更高效地探索有希望的解子空间。

实验结果

研究问题

• RQ1 全知的、时间感知的动态装箱模型是否能减少私有云环境中所需的服务器数量？
• RQ2 DCBB算法在收敛速度和解质量方面与传统分支定界算法相比如何？
• RQ3 改进的首次适应和蚁群算法在多维异构CDBP环境中有多大有效性？
• RQ4 在真实工作负载下，DCBB算法是否比原始分支定界算法更快地获得最优解？
• RQ5 所提出的模型是否能显著降低私有云部署的前期基础设施成本？

主要发现

• 与原始分支定界算法相比，DCBB算法在真实工作负载下以快一个数量级的运行时间实现了最优解。
• DCBB算法在收敛速度上远超其他基于搜索的算法，包括改进的首次适应和蚁群系统方法。
• CDBP模型能有效减少所需服务器数量，直接降低私有云基础设施的前期成本。
• 所提方法在大幅提升处理速度的同时保持了高解质量，适用于实时或近实时的虚拟机部署。
• 在合成与真实数据上的实验表明，DCBB在效率和解的最优性方面均优于基线算法。
• 时间感知调度的集成实现了异构、多维环境中的更好资源利用率和更低碎片化。