Disaggregated Computing. An Evaluation of Current Trends for Datacentres

Open Access
Authors
  • H. Meyer ORCID logo
  • J.C. Sancho
  • J.V. Quiroga
  • F. Zyulkyarov
  • D. Roca
  • M. Nemirovsky
Publication date 2017
Journal Procedia Computer Science
Event International Conference on Computational Science 2017
Volume | Issue number 108
Pages (from-to) 685-694
Organisations
  • Faculty of Science (FNWI) - Informatics Institute (IVI)
Abstract
Next generation data centers will likely be based on the emerging paradigm of disaggregated function-blocks-as-a-unit departing from the current state of mainboard-as-a-unit. Multiple functional blocks or bricks such as compute, memory and peripheral will be spread through the entire system and interconnected together via one or multiple high speed networks. The amount of memory available will be very large distributed among multiple bricks. This new architecture brings various benefits that are desirable in today’s data centers such as fine-grained technology upgrade cycles, fine-grained resource allocation, and access to a larger amount of memory and accelerators. An analysis of the impact and benefits of memory disaggregation is presented in this paper. One of the biggest challenges when analyzing these architectures is that memory accesses should be modeled correctly in order to obtain accurate results. However, modeling every memory access would generate a high overhead that can make the simulation unfeasible for real data center applications. A model to represent and analyze memory disaggregation has been designed and a statistics-based queuing-based full system simulator was developed to rapidly and accurately analyze applications performance in disaggregated systems. With a mean error of 10%, simulation results pointed out that the network layers may introduce overheads that degrade applications’ performance up to 66%. Initial results also suggest that low memory access bandwidth may degrade up to 20% applications’ performance.
Document type Article
Note International Conference on Computational Science, ICCS 2017, 12-14 June 2017, Zurich, Switzerland Edited by Petros Koumoutsakos, Michael Lees, Valeria Krzhizhanovskaya, Jack Dongarra, Peter Sloot
Language English
Published at https://doi.org/10.1016/j.procs.2017.05.129
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