Flexspander: augmenting expander networks in high-performance systems with optical bandwidth steering

Communication efficiency is one of the deciding factors in determining many of today’s high-performance computing (HPC) applications. Traditionally, HPC systems have been on static network topologies, making them inflexible to the variety of skewed traffic demands that may arise due to the spatial locality inherent in many applications. To handle traffic locality, researchers have proposed integrating optical circuit switches (OCSs) into the network architecture, which reconfigures the network topology to alter and dynamically adapt to the predicted traffic. In this paper, we present a novel reconfigurable network topology called Flexspander. Beyond offering a flexible interconnect, Flexspander also offers full flexibility in terms of construction and can be built with any arbitrary combination of commercial electrical packet switches and OCSs. We evaluate Flexspander performance through extensive simulations with multiple network traces, and our results show improved performance for Flexspander over currently proposed static and reconfigurable topologies in terms of the flow completion time.

[1]  Michael Dinitz,et al.  Xpander: Towards Optimal-Performance Datacenters , 2016, CoNEXT.

[2]  Nick McKeown,et al.  pFabric: minimal near-optimal datacenter transport , 2013, SIGCOMM.

[3]  Paramvir Bahl,et al.  Flyways To De-Congest Data Center Networks , 2009, HotNets.

[4]  Zongpeng Li,et al.  sFlow: towards resource-efficient and agile service federation in service overlay networks , 2004, 24th International Conference on Distributed Computing Systems, 2004. Proceedings..

[5]  C SnoerenAlex,et al.  Inside the Social Network's (Datacenter) Network , 2015 .

[6]  Gal Shahaf,et al.  Beyond fat-trees without antennae, mirrors, and disco-balls , 2017, SIGCOMM.

[7]  Michael Dinitz,et al.  Xpander: Unveiling the Secrets of High-Performance Datacenters , 2015, HotNets.

[8]  Avi Wigderson,et al.  Entropy waves, the zig-zag graph product, and new constant-degree expanders and extractors , 2000, Proceedings 41st Annual Symposium on Foundations of Computer Science.

[9]  Charles E. Leiserson,et al.  Fat-trees: Universal networks for hardware-efficient supercomputing , 1985, IEEE Transactions on Computers.

[10]  Amin Vahdat,et al.  Helios: a hybrid electrical/optical switch architecture for modular data centers , 2010, SIGCOMM '10.

[11]  William J. Dally,et al.  Technology-Driven, Highly-Scalable Dragonfly Topology , 2008, 2008 International Symposium on Computer Architecture.

[12]  John Shalf,et al.  Bandwidth steering in HPC using silicon nanophotonics , 2019, SC.

[13]  Keren Bergman,et al.  Design Space Exploration of the Dragonfly Topology , 2017, ISC Workshops.

[14]  Alex C. Snoeren,et al.  Inside the Social Network's (Datacenter) Network , 2015, Comput. Commun. Rev..

[15]  Jung Ho Ahn,et al.  HyperX: topology, routing, and packaging of efficient large-scale networks , 2009, Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis.

[16]  Ankit Singla,et al.  Jellyfish: Networking Data Centers Randomly , 2011, NSDI.

[17]  Amin Vahdat,et al.  A scalable, commodity data center network architecture , 2008, SIGCOMM '08.

[18]  Leonid Oliker,et al.  Reconfigurable hybrid interconnection for static and dynamic scientific applications , 2007, CF '07.

[19]  Nikhil R. Devanur,et al.  ProjecToR: Agile Reconfigurable Data Center Interconnect , 2016, SIGCOMM.

[20]  Ming Zhang,et al.  Understanding data center traffic characteristics , 2010, CCRV.

[21]  Himanshu Shah,et al.  FireFly , 2014, SIGCOMM.

[22]  U. Feige,et al.  Spectral Graph Theory , 2015 .

[23]  Rui Zhang-Shen,et al.  Valiant Load-Balancing: Building Networks That Can Support All Traffic Matrices , 2010, Algorithms for Next Generation Networks.

[24]  Stefan Schmid,et al.  Characterizing the algorithmic complexity of reconfigurable data center architectures , 2018, ANCS.

[25]  VahdatAmin,et al.  Integrating microsecond circuit switching into the data center , 2013 .

[26]  Albert G. Greenberg,et al.  VL2: a scalable and flexible data center network , 2009, SIGCOMM '09.

[27]  VahdatAmin,et al.  A scalable, commodity data center network architecture , 2008 .

[28]  Keren Bergman,et al.  Flexfly: Enabling a Reconfigurable Dragonfly through Silicon Photonics , 2016, SC16: International Conference for High Performance Computing, Networking, Storage and Analysis.

[29]  Mike Higgins,et al.  Cray Cascade: A scalable HPC system based on a Dragonfly network , 2012, 2012 International Conference for High Performance Computing, Networking, Storage and Analysis.

[30]  Flyways To DeCongest Data Center Networks , 2009 .

[31]  J. Y. Yen,et al.  Finding the K Shortest Loopless Paths in a Network , 2007 .

[32]  Albert G. Greenberg,et al.  The nature of data center traffic: measurements & analysis , 2009, IMC '09.

[33]  David A. Maltz,et al.  Network traffic characteristics of data centers in the wild , 2010, IMC '10.

[34]  Min Zhu,et al.  B4: experience with a globally-deployed software defined wan , 2013, SIGCOMM.

[35]  M. Murty Ramanujan Graphs , 1965 .

[36]  Antony I. T. Rowstron,et al.  Larry: Practical Network Reconfigurability in the Data Center , 2018, NSDI.

[37]  Ben Y. Zhao,et al.  Mirror mirror on the ceiling: flexible wireless links for data centers , 2012, CCRV.

[38]  Mark Jerrum,et al.  Three-Dimensional Statistical Data Security Problems , 1994, SIAM J. Comput..

[39]  Konstantina Papagiannaki,et al.  c-Through: part-time optics in data centers , 2010, SIGCOMM '10.

[40]  Amin Vahdat,et al.  Integrating microsecond circuit switching into the data center , 2013, SIGCOMM.