Issue No. 04 - April (2017 vol. 28)
Zhen Zhang , Department of Computer Science, Jinan University, Guangzhou, Guangdong, China
Yuhui Deng , Department of Computer Science, Jinan University, Guangzhou, Guangdong, China
Geyong Min , College of Engineering, Mathematics, and Physical Sciences, University of Exeter, Exeter, United Kingdom
Junjie Xie , Department of Computer Science, Jinan University, Guangzhou, Guangdong, China
Shuqiang Huang , Network and Educational Technology Center, Jinan University, Guangzhou, Guangdong, China
Over the past decade, many data centers have been constructed around the world due to the explosive growth of data volume and type. The cost and energy consumption have become the most important challenges of building those data centers. Data centers today use commodity computers and switches instead of high-end servers and interconnections for cost-effectiveness. In this paper, we propose a new type of interconnection networks called
Exchanged Cube-Connected Cycles (ExCCC). The ExCCC network is an extension of Exchanged Hypercube (EH) network by replacing each node with a cycle. The EH network is based on link removal from a Hypercube network, which makes the EH network more cost-effective as it scales up. After analyzing the topological properties of ExCCC, we employ commodity switches to construct a new class of data center network models, namely ExCCC-DCN, by leveraging the advantages of the ExCCC architecture. The analysis and experimental results demonstrate that the proposed ExCCC-DCN models significantly outperform four state-of-the-art data center network models in terms of the total cost, power consumption, scalability, and other static characteristics. It achieves the goals of low cost, low energy consumption, high network throughput, and high scalability simultaneously.
Servers, Ports (Computers), Hypercubes, Data models, Network topology, Analytical models
Z. Zhang, Y. Deng, G. Min, J. Xie and S. Huang, "ExCCC-DCN: A Highly Scalable, Cost-Effective and Energy-Efficient Data Center Structure," in IEEE Transactions on Parallel & Distributed Systems, vol. 28, no. 4, pp. 1046-1060, 2017.