Palpatine: Mining Frequent Sequences for Data Prefetching in NoSQL Distributed Key-Value Stores

This paper presents Palpatine, the first in-memory application-level cache for Distributed Key-Value (DKV) data stores, capable of prefetching data that is likely to be accessed in an immediate future. To predict data accesses, Palpatine continuously captures frequent access patterns to the back store by means of data mining techniques. With these patterns, Palpatine builds a stochastic graph of accessed items, and makes prefetching decisions based on it. Experimental evaluation indicates that Palpatine can improve the latency of a specific DKV store by more that an order of magnitude.