Dissertation Defense

Disaggregated Memory Architectures for Blade Servers

Kevin Te-Ming Lim

Current trends in memory capacity and power in servers indicate the need for memory system redesign. Memory capacity is projected to grow at a smaller rate relative to the growth in compute capacity, and memory power is a substantial and growing portion of server power budgets. As these capacity and power trends continue, a new memory architecture is needed that provides increased capacity and maximize resource efficiency.
This thesis presents the design of a disaggregated memory architecture for blade servers that provides expanded memory capacity and dynamic capacity sharing across multiple servers. The design disaggregates a portion of the servers? memory, which is then assembled in separate memory blades optimized for capacity and power usage. The servers access memory blades through a redesigned memory hierarchy that is extended to include a remote level. Through the shared interconnect of blade enclosures, multiple compute blades can dynamically share a memory blade?s capacity. Two system architectures are evaluated that provide operating system-transparent access to the memory blades. Finally, by extending the principles of disaggregation, new server architectures are proposed that provide substantial performance-per-cost benefits for large-scale data centers over traditional servers.

Sponsored by

T. Mudge and S. Reinhardt