2020/02/01 by Sergio Esteves, S. N. Esteves, João Nuno Silva +6
Computer Science · #Cloud Computing and Resource Management #Databases (cs.DB) #Distributed #Distributed systems and fault tolerance #FOS: Computer and information sciences #Parallel #Software System Performance and Reliability #and Cluster Computing (cs.DC) #cs.DB #cs.DC
paper · pdf · doi:10.48550/arxiv.2002.00215
14 pages, 27 figures
openalex publication_date 2020/02/01 · arxiv created 2020/04/22 · arxiv updated 2020/04/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.