Disaggregated memory for expansion and sharing in blade servers

  • Authors:

  • Kevin Lim;Jichuan Chang;Trevor Mudge;Parthasarathy Ranganathan;Steven K. Reinhardt;Thomas F. Wenisch

  • Affiliations:

  • University of Michigan, Ann Arbor, MI, USA;Hewlett-Packard Labs, Palo Alto, CA, USA;University of Michigan, Ann Arbor, MI, USA;Hewlett-Packard Labs, Palo Alto, CA, USA;Advanced Micro Devices, Inc., Bellevue, USA;University of Michigan, Ann Arbor, MI, USA

  • Venue:
  • Proceedings of the 36th annual international symposium on Computer architecture
  • Year:
  • 2009

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Abstract

Analysis of technology and application trends reveals a growing imbalance in the peak compute-to-memory-capacity ratio for future servers. At the same time, the fraction contributed by memory systems to total datacenter costs and power consumption during typical usage is increasing. In response to these trends, this paper re-examines traditional compute-memory co-location on a single system and details the design of a new general-purpose architectural building block-a memory blade-that allows memory to be "disaggregated" across a system ensemble. This remote memory blade can be used for memory capacity expansion to improve performance and for sharing memory across servers to reduce provisioning and power costs. We use this memory blade building block to propose two new system architecture solutions-(1) page-swapped remote memory at the virtualization layer, and (2) block-access remote memory with support in the coherence hardware-that enable transparent memory expansion and sharing on commodity-based systems. Using simulations of a mix of enterprise benchmarks supplemented with traces from live datacenters, we demonstrate that memory disaggregation can provide substantial performance benefits (on average 10X) in memory constrained environments, while the sharing enabled by our solutions can improve performance-per-dollar by up to 57% when optimizing memory provisioning across multiple servers.