面向云数据中心的可扩展流量感知虚拟机管理

Fung Po Tso, K. Oikonomou, Eleni Kavvadia, D. Pezaros
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引用次数: 30

摘要

虚拟机管理是在云数据中心(DC)上提供弹性服务的强大机制。与此同时,由此产生的网络拥塞被反复报道为数据中心的主要瓶颈,即使在基础设施的总体资源利用率仍然很低的情况下也是如此。然而,大多数当前的VM管理策略是流量不可知的,而少数流量感知策略只关注静态初始分配,忽略带宽超额订阅,或者不扩展。在本文中,我们提出了S-CORE,一种可扩展的虚拟机迁移算法,可以动态地将虚拟机重新分配到服务器,同时最大限度地减少活动流量的总体通信占用。我们将聚合虚拟机通信表述为一个优化问题,然后我们定义了一种新的分布式迁移方案,该方案迭代地适应动态流量变化。通过广泛的仿真和实现结果,我们表明S-CORE在产生最小开销和停机时间的同时实现了显著(高达87%)的通信成本降低。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Scalable Traffic-Aware Virtual Machine Management for Cloud Data Centers
Virtual Machine (VM) management is a powerful mechanism for providing elastic services over Cloud Data Centers (DC)s. At the same time, the resulting network congestion has been repeatedly reported as the main bottleneck in DCs, even when the overall resource utilization of the infrastructure remains low. However, most current VM management strategies are traffic-agnostic, while the few that are traffic-aware only concern a static initial allocation, ignore bandwidth oversubscription, or do not scale. In this paper we present S-CORE, a scalable VM migration algorithm to dynamically reallocate VMs to servers while minimizing the overall communication footprint of active traffic flows. We formulate the aggregate VM communication as an optimization problem and we then define a novel distributed migration scheme that iteratively adapts to dynamic traffic changes. Through extensive simulation and implementation results, we show that S-CORE achieves significant (up to 87%) communication cost reduction while incurring minimal overhead and downtime.
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