Optimization of Small Updates for Erasure-Coded In-memory Stores

Jianzhong Huang, Jie Xia, Xiao Qin, Qiang Cao, Changsheng Xie

Abstract: Data updates have become an important issue in erasure-coded in-memory stores owing to the two-fold reasons: (i) a handful of data-intensive in-memory stores adopt erasure coding for ‘hot’ data and (ii) small writes in update-intensive in-memory workloads cause expensive updating overheads. After delving into prior updating schemes in erasure-coded storage clusters, we investigate the applicability of these schemes to erasure-coded in-memory stores. We propose a grouped-updating 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 an updating group can be concurrently executed. Two GU updating procedures—GU-stripe and GU-node—are developed to schedule updates according to a stripe and a node holding an updated data block, respectively. Furthermore, we develop two hybrid-updating schemes—Hybrid−U[GU-stripe] and Hybrid−U[GU-node]—to process common writes (i.e. small and large writes) initiated by the GU-stripe- and GU-node-based updating schemes, respectively. Replaying an update-heavy workload generated by YCSB benchmark, we extensively evaluate the four non-GU-based updating schemes, five GU-stripe-based updating schemes, and five GU-node-based updating schemes. Our experiments demonstrate that the GU mechanism boosts updating performance of small writes for RS-coded in-memory stores in terms of updating time and updating traffic. In particular, for a (8, 6) RS-coded in-memory store, the GU-stripe- and GU-node-based updating schemes shortens the updating time of the non-GU-based counterparts by a factor of at least 2.08 and 2.66, respectively. Compared to a single GU-based updating scheme, a GU-based hybrid updating scheme achieves an optimal updating-time and updating-traffic performance.