PTNet: An efficient and green data center network

Abstract : In recent years, data centers have witnessed an exponential growth for hosting hundreds of thousands of servers as well as to accommodating a very large demand for resources. To fulfill the required level of demand, some approaches tackled network aspects so to host a huge number of servers while others focused on delivering rapid services to the clients by minimizing the path length between any two servers. In general, network devices are often designed to achieve 1:1 oversubscription. Alternatively, in a realistic data center environment, the average utilization of a network could vary between 5% and 25%, and thus the energy consumed by idle devices is wasted. This paper proposes a new parameterizable data center topology, called PTNet. PTNet offers a gradual scalability that interconnects small to large networks covering different ranges of sizes. This new interconnection network provides also a small path length between any two servers even in large sized data centers. PTNet does not only reduce path length and latency, it also uses a power-aware routing algorithm which saves up to 40% of energy with an acceptable computation time. In comparison to existing solutions (e.g. Flatnet, BCube, DCell and Fat-tree), PTNet shows substantial improvements in terms of capacity, robustness, cost-effectiveness and power efficiency: this improvement reaches up to 50% in some cases. (C) 2017 Elsevier Inc. All rights reserved.
Type de document :
Article dans une revue
Journal of Parallel and Distributed Computing, Elsevier, 2017, 107, pp.3-18. 〈http://www.sciencedirect.com/science/article/pii/S0743731517300953?via%3Dihub〉. 〈10.1016/j.jpdc.2017.03.007 〉
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Contributeur : Le2i - Université de Bourgogne <>
Soumis le : jeudi 20 juillet 2017 - 14:50:46
Dernière modification le : mardi 6 février 2018 - 15:56:21

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Emna Baccour, Sebti Foufou, Ridha Hamila, Zahir Tari, A. Y. Zomaya. PTNet: An efficient and green data center network. Journal of Parallel and Distributed Computing, Elsevier, 2017, 107, pp.3-18. 〈http://www.sciencedirect.com/science/article/pii/S0743731517300953?via%3Dihub〉. 〈10.1016/j.jpdc.2017.03.007 〉. 〈hal-01565972〉

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