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short-paper

Scaling of capacity and reliability in data center networks

Published: 04 September 2014 Publication History

Abstract

The traditional connectivity model within the data center is that of a hierarchical tree with redundant connections (\fat tree") and with a top node consisting of one or more routers that bring in (and send out completed) requests for processing. In this paper we examine alternative connectivity models for large-scale data centers. In the first model, we examine hypergrids as the structure connecting switches and routers to the edge server racks. In the second model, we examine random graphs as the interconnecting network. We compare and contrast the capacity, congestion and reliability requirements for these relative to fat-trees. We show that, as the system size increases and for uniform switch-end-to- switch-end demand, the fat-tree configuration emerges as an expensive option demanding higher port density switches but has low congestion and high reliability. In contrast, the random graph model shows the same low level of congestion, lower cost due to fewer ports and reasonable reliability, whereas the hypergrid model does not require scaling of switch ports, provides high reliability but exhibits higher congestion.

References

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A. Greenberg, J. R. Hamilton, N. Jain, S. Kandula, C. Kim, parantap Lahiri, D. A. Maltz, P. patel, and S. Sengupta, "Vl2: A scalable and exible data center network," ACM SIGCOMM, pp. 51--62, August 2009.
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A. Singlu, C.-Y. Hong, L. Popa, and P. B. Godfrey, "Jelly sh: Networking data centers randomly," NSDI, pp. 225--238, April 2012.
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J. Mudigonda, P. Yalagandula, and J. C. Mogul, "Taming the ying cable monster: A topology design and optimization framework for data-center networks," USENIX ATC, pp. 101--114, June 2011.
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O. Narayan and I. Saniee, "Large-scale curvature of networks," Phys. Rev. E, vol. 84, p. 066108, Dec 2011.
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M. Csernai, A. Gulyás, A. Korosi, B. Sonkoly, and G. Biczok, "Poincare: A hyperbolic data center architecture," 2012 32nd International Conference on Distributed Computing Systems Workshops, vol. 0, pp. 8--16, 2012.

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        Salvatore F. Pileggi

        The interconnection of servers has a critical impact on data center performance, so the internal network requires an efficient design. As data center sizes scale up, a significant tradeoff between performance and cost emerges. Indeed, an optimized network design, addressing scalability requirements in real environments, mostly tries to balance that key tradeoff. In this paper, the authors provide a simple perspective to analyze network performance in high-scale data centers as a function of the cost. Three different classes of solutions are considered in order to compare traditional designs (fat tree) with evolving approaches (hypergrid and random graph). The paper is smart and concise. Despite the strongly technical focus, it should be understandable to a wide audience, probably because of the relatively simple models and metrics adopted (capacity, congestion, and reliability). Even though the paper covers the topic exhaustively, I would have appreciated an extensive discussion about the hybrid solutions between hypergrid and random graph, which, according to the authors, are expected to outperform all topologies on all metrics. Considering the high quality of this contribution, I hope to enjoy an extended version very soon. Online Computing Reviews Service

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        Published In

        cover image ACM SIGMETRICS Performance Evaluation Review
        ACM SIGMETRICS Performance Evaluation Review  Volume 42, Issue 2
        September 2014
        74 pages
        ISSN:0163-5999
        DOI:10.1145/2667522
        Issue’s Table of Contents

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        Published: 04 September 2014
        Published in SIGMETRICS Volume 42, Issue 2

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        Author Tags

        1. capacity
        2. data center networks
        3. fat-trees
        4. hypergrids
        5. random graphs
        6. reliability

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        • (2019)QoS for SDN-Based Fat-Tree Networks10.1007/978-3-030-12385-7_49(691-705)Online publication date: 2-Feb-2019
        • (2018)A locality-aware shuffle optimization on fat-tree data centersFuture Generation Computer Systems10.1016/j.future.2018.06.01689(31-43)Online publication date: Dec-2018
        • (2018)Service disruption index (SDI)Transactions on Emerging Telecommunications Technologies10.1002/ett.349230:3Online publication date: 6-Aug-2018
        • (2017)A Simple Congestion-Aware Algorithm for Load Balancing in Datacenter NetworksIEEE/ACM Transactions on Networking10.1109/TNET.2017.275125125:6(3670-3682)Online publication date: 1-Dec-2017
        • (2017)Similarity-Based Node Distance Exploring and Locality-Aware Shuffle Optimization for Hadoop MapReduce2017 IEEE International Conference on Smart Cloud (SmartCloud)10.1109/SmartCloud.2017.23(103-108)Online publication date: Nov-2017
        • (2017)Software-defined extreme scale networks for bigdata applications2017 IEEE High Performance Extreme Computing Conference (HPEC)10.1109/HPEC.2017.8091087(1-7)Online publication date: Sep-2017
        • (2017)AdaptScale: An adaptive data scaling controller for improving the multiple performance requirements in CloudsFuture Generation Computer Systems10.1016/j.future.2017.08.034Online publication date: Sep-2017
        • (2015)Design Methodology for Optimizing Optical Interconnection Networks in High Performance SystemsHigh Performance Computing10.1007/978-3-319-20119-1_32(454-471)Online publication date: 20-Jun-2015
        • (undefined)A simple congestion-aware algorithm for load balancing in datacenter networksIEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications10.1109/INFOCOM.2016.7524468(1-9)

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