ANTELOPE: A Semantic-Aware Data Cube Scheme for Cloud Data Center Networks
ANTELOPE: A Semantic-Aware Data Cube Scheme for Cloud Data Center Networks
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DOI：
10.1109/tc.2013.110
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发表时间：
2014-09
影响因子：
3.7
通讯作者：
Yu Hua;Xue Liu;Hong Jiang
中科院分区：
文献类型：
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作者：
Yu Hua;Xue Liu;Hong Jiang
今天的云数据中心包含数百万台服务器，并提供高带宽。云计算中的一个根本问题是如何显著提高大规模系统的可扩展性，以实现大量服务器的互连，同时支持各种在线服务。一种方法是处理网络架构和数据放置之间的潜在不匹配的挑战。为了应对这一挑战，我们提出了羚羊，一个可扩展的分布式云数据中心的数据为中心的计划，在其中，我们系统地考虑到网络架构的属性和数据放置的优化。羚羊背后的基本思想是利用基于预计算的数据立方体来支持在线云服务。由于数据立方体的构建受到完全物化的高成本的影响，我们使用语义感知的部分物化解决方案，以显着减少操作和空间开销。在真实的系统实现上的大量实验证明了我们所提出的方案的有效性和效率。
Today's cloud data centers contain more than millions of servers and offer high bandwidth. A fundamental problem is how to significantly improve the large-scale system's scalability to interconnect a large number of servers and meanwhile support various online services in cloud computing. One way is to deal with the challenge of potential mismatching between the network architecture and the data placement. To address this challenge, we present ANTELOPE, a scalable distributed data-centric scheme in cloud data centers, in which we systematically take into account both the property of network architecture and the optimization of data placement. The basic idea behind ANTELOPE is to leverage precomputation based data cube to support online cloud services. Since the construction of data cube suffers from the high costs of full materialization, we use a semantic-aware partial materialization solution to significantly reduce the operation and space overheads. Extensive experiments on real system implementations demonstrate the efficacy and efficiency of our proposed scheme.