CAREER: End-to-End Network Design for Unified Memory Disaggregation

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Applications in modern cloud datacenters are deployed in resource containers to isolate them from each other. Memory stranding is a pervasive problem in such containerized datacenters, where many memory-intensive applications grind to a halt even when free memory exists in other machines. This leads to low utilization, memory fragmentation, and overall increased cost. Memory disaggregation over ultra-fast networks can pool together such stranded memory in theory, but making it practical faces novel systems design, algorithmic, and integration challenges. They include bridging the still-sizable latency gap between local memory access vs. Remote Direct Memory Access (RDMA), transparently addressing network-wide fault-tolerance, load imbalance, and performance isolation issues, scalability, and enabling support for heterogeneous software and hardware technologies.The overarching research objective of this proposal is to realize a Unified Disaggregated Memory (UDM) abstraction over ultra-fast networks to expose stranded memory across the datacenter as a pool of available memory to out-of-memory containers in a fast, resilient, and scalable manner without any changes to the applications. By designing a comprehensive solution to address host-level, network-level, and end-to-end aspects of the aforementioned challenges, this research aims to make memory disaggregation practical. Specifically, by leveraging the unique characteristics of memory-intensive workloads, ultra-low-latency networks, and multi-tenancy in modern datacenters, this proposal will (i) design a low-latency host networking stack; (ii) enable performance isolation throughout the network; (iii) provide resilience to network-wide uncertainties such as failures and load imbalance; and (iv) incorporate support for heterogeneous memory (e.g., persistent memory), networking technologies, and resource management software.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.