Causal consistency (CC) is an attractive consistency model for geo-replicated data stores
because it hits a sweet spot in the ease-of-programming versus performance trade-off. We …

The Hadoop Distributed File System (HDFS) is a distributed storage system that stores large
volumes of data reliably and provide access to the data by the applications at high …

This article is an eight-year retrospective on development priorities for RocksDB, a key-value
store developed at Facebook that targets large-scale distributed systems and that is …

Distributed database systems are widely used to meet the demands of storing and
managing computation-heavy workloads. To boost performance and minimize resource and …

In this paper we describe the design and implementation of ACaZoo, a key-value store that
combines strong consistency with high performance and high availability. ACaZoo supports …

Reconfiguring a cloud storage system can improve its overall service. Tuba is a geo-
replicated key-value store that automatically reconfigures its set of replicas while respecting …

Applications of key-value (KV-) storage often exhibit high spatial locality, such as when
many data items have identical composite key prefixes. This prevalent access pattern is …

Cluster-based storage systems are popular for data-intensive applications and it is desirable
yet challenging to provide incremental expansion and high availability while achieving …

Hybrid parallel file systems (PFS), consisting of multiple HDD and SSD I/O servers, provide a
promising design for data intensive applications. The efficiency of a hybrid PFS relies on the …

Key-value stores such as LevelDB and RocksDB offer excellent write throughput, but suffer
high write amplification. The write amplification problem is due to the Log-Structured Merge …