Revisiting Updating Schemes for Erasure-Coded In-Memory Stores

Erasure coding has been gradually adopted by existing data-intensive in-memory stores for 'hot' data; small writes lead to expensive updating overheads in such in-memory stores characterized by update-heavy workloads. There is a pressing demand to address the issue of data updates for erasure-coded in-memory stores. We revisit existing updating schemes in erasure-coded storage clusters by investigating the applicability of these updating schemes to erasure-coded in- memory stores. After an intensive analysis, we propose a grouping-update mechanism - GU - to handle small writes in in-memory stores. With GU in place, requests in an updating window are categorized into several updating groups, where multiple small updates in the same stripe can be executed concurrently. Furthermore, we bring forward a hybrid-updating scheme - Hybrid-U - to minimize total updating I/Os over network under common writes (e.g., small and large writes). We evaluate four dedicated updating schemes, four GU- based updating schemes and Hybrid-U. Our experiments illustrate that GU-based updating schemes and Hybrid-U outperform the four dedicated updating schemes in terms of updating time.