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# Fast RDMA-based Ordered Key-Value Store using Remote Learned Cache

**Research question**: RDMA for ordered key-value stores.

**Current approaches**: *Index caching* to reduce RDMA operations. (1) The tree-based index can be large, so that the cache would suffer from unavoidable capacity misses. (2) The cache would aggravate random memory accesses and further increase the end-to-end latency. (3) Updating the tree-based index may recursively invalidate the cache and cause false invalidation due to path sharing.

**Idea**: leverage ML models as the (client-side) RDMA-based cache for the (server-side) tree-based index.

**Challenges**: Although using ML models as the index seems efficient (a few floating/int operations) and cheap (a small memory footprint) for **static workloads** (e.g., gets), it is also notoriously slow (frequently retraining ML models) and costly (keeping data in order) for **dynamic workload**s (e.g., inserts) => Hybrid architecture for static and dynamic workloads.