Kavli Affiliate: Jing Wang
| First 5 Authors: Jing Wang, Chao Li, Taolei Wang, Jinyang Guo, Hanzhang Yang
| Summary:
The growing scale of data requires efficient memory subsystems with large
memory capacity and high memory performance. Disaggregated architecture has
become a promising solution for today’s cloud and edge computing for its
scalability and elasticity. As a critical part of disaggregation, disaggregated
memory faces many design challenges in many dimensions, including hardware
scalability, architecture structure, software system design, application
programmability, resource allocation, power management, etc. These challenges
inspire a number of novel solutions at different system levels to improve
system efficiency. In this paper, we provide a comprehensive review of
disaggregated memory, including the methodology and technologies of
disaggregated memory system foundation, optimization, and management. We study
the technical essentials of disaggregated memory systems and analyze them from
the hardware, architecture, system, and application levels. Then, we compare
the design details of typical cross-layer designs on disaggregated memory.
Finally, we discuss the challenges and opportunities of future disaggregated
memory works that serve better for next-generation elastic and efficient
datacenters.
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