VDC Planner: Dynamic migration-aware Virtual Data Center embedding for clouds
VDC Planner: Dynamic migration-aware Virtual Data Center embedding for clouds
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发表时间：
2013-05
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通讯作者：
M. Zhani;Qi Zhang;G. Simon;R. Boutaba
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作者：
M. Zhani;Qi Zhang;G. Simon;R. Boutaba
云计算承诺以按需方式向大量服务应用提供计算资源。传统上，像Amazon这样的云提供商只为计算和存储资源提供有保证的分配，而无法支持这些应用程序之间的带宽需求和性能隔离。为了解决这一限制，最近，许多提案主张以虚拟数据中心（VDC）的形式提供有保证的服务器和网络资源。这就提出了将服务器和数据中心网络最佳分配给多个VDC的问题，以便最大化总收入，同时最小化数据中心的总能耗。然而，尽管最近对这个问题进行了研究，但是现有的解决方案都没有考虑使用VM迁移来动态调整资源分配的可能性，以满足VDC波动的资源需求。在本文中，我们提出了VDC规划，迁移感知的动态虚拟数据中心嵌入框架，旨在实现高收入，同时最大限度地减少总能源成本随着时间的推移。我们的框架支持各种使用场景，包括VDC嵌入、VDC扩展以及动态VDC整合。通过使用现实的工作负载跟踪实验，我们表明，我们提出的方法实现了更高的收入和更低的平均调度延迟相比，现有的迁移无关的解决方案。
Cloud computing promises to provide computing resources to a large number of service applications in an on demand manner. Traditionally, cloud providers such as Amazon only provide guaranteed allocation for compute and storage resources, and fail to support bandwidth requirements and performance isolation among these applications. To address this limitation, recently, a number of proposals advocate providing both guaranteed server and network resources in the form of Virtual Data Centers (VDCs). This raises the problem of optimally allocating both servers and data center networks to multiple VDCs in order to maximize the total revenue, while minimizing the total energy consumption in the data center. However, despite recent studies on this problem, none of the existing solutions have considered the possibility of using VM migration to dynamically adjust the resource allocation, in order to meet the fluctuating resource demand of VDCs. In this paper, we propose VDC Planner, a migration-aware dynamic virtual data center embedding framework that aims at achieving high revenue while minimizing the total energy cost over-time. Our framework supports various usage scenarios, including VDC embedding, VDC scaling as well as dynamic VDC consolidation. Through experiments using realistic workload traces, we show our proposed approach achieves both higher revenue and lower average scheduling delay compared to existing migration-oblivious solutions.