We present RQFTL, a demand-based FTL for mobile storage controllers that boosts the effective Logical-To-Physical (L2P) address translation cache capacity over state-of-the-art techniques. RQFTL stores a large part of the L2P cache in a compressed form, and employs a learned data structure called RQRMI that leverages tiny neural nets to quickly find the correct translation entry in the cache. RQFTL uses neural network inference for cache lookups, and rapidly retrains the neural nets to efficiently handle L2P cache updates. It is specifically optimized to achieve high coverage for scattered read accesses, making it suitable for popular read-skewed workloads such as mobile gaming.

Virtualization has become a critical aspect of modern computing, and with the advent of virtualization-based containers, fast nested virtualization has become increasingly important. Nested virtualization is implemented by emulating virtualization capabilities to the guest host which can result in significant overhead. Another source of overheads in virtualization stems from the address translation mechanisms employed to implement virtualization, which usually causes a mix of slower address translation, frequently trapping guests, and loss of granularity in page tables. Our research focuses on using guest-managed physical memory with the use of per-VM memory tags for checking each VMs' access permissions.