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High cache miss cost for caching tree Tree node size can be much larger than the KV
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Fast RDMA-based Ordered Key-Value Store Using Remote Learned Cache
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- 1 Intro
- 2 KVS: key pillar for distributed systems
- 3 Traditional KVS uses RPC (Server-centric)
- 4 Challenge: limited NIC abstraction
- 5 Existing systems adopt caching
- 6 High cache miss cost for caching tree Tree node size can be much larger than the KV
- 7 Trade-off of existing KVS
- 8 Overview of XSTORE Hybrid architecture 11
- 9 Our approach: Learned cache Using ML as the cache structure for tree-based index Motivated by the learned index[1]
- 10 Client-direct Get() using learned cache
- 11 Benefits of the learned cache
- 12 Challenges of learned cache
- 13 Outline of the remaining content Server-side data structure for dynamic workloads
- 14 Models cannot learn dynamic B+Tree address Can only learn when the addresses are sorted
- 15 Solution: another layer of indirection Observation: leaf nodes are logically sorted
- 16 Client-direct Get() using model & TT
- 17 Model retraining Model is retrained at server in background threads 9: Small cost & extra CPU usage at the server
- 18 Stale model handling Background update causes stale learned models
- 19 Performance of XSTORE on YCSB 100M KVS, uniform workloads
- 20 Sensitive to the dataset
