Can far memory improve job throughput?

Emmanuel Amaro, Christopher Branner-Augmon, Zhihong Luo, Amy Ousterhout, Marcos K. Aguilera, Aurojit Panda, Sylvia Ratnasamy, Scott Shenker

Research output: Chapter in Book/Report/Conference proceedingConference contribution


As memory requirements grow, and advances in memory technology slow, the availability of sufficient main memory is increasingly the bottleneck in large compute clusters. One solution to this is memory disaggregation, where jobs can remotely access memory on other servers, or far memory. This paper first presents faster swapping mechanisms and a far memory-Aware cluster scheduler that make it possible to support far memory at rack scale. Then, it examines the conditions under which this use of far memory can increase job throughput. We find that while far memory is not a panacea, for memory-intensive workloads it can provide performance improvements on the order of 10% or more even without changing the total amount of memory available.

Original languageEnglish (US)
Title of host publicationProceedings of the 15th European Conference on Computer Systems, EuroSys 2020
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9781450368827
StatePublished - Apr 15 2020
Event15th European Conference on Computer Systems, EuroSys 2020 - Heraklion, Greece
Duration: Apr 27 2020Apr 30 2020

Publication series

NameProceedings of the 15th European Conference on Computer Systems, EuroSys 2020


Conference15th European Conference on Computer Systems, EuroSys 2020

ASJC Scopus subject areas

  • Information Systems
  • Computer Networks and Communications
  • Hardware and Architecture
  • Software
  • Control and Systems Engineering


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