This article addresses the problem of self-tuning the data placement in replicated key-value
stores. The goal is to automatically optimize replica placement in a way that leverages …

Owing to a need for low latency data accesses, emerging IoT and mobile applications
commonly require distributed data stores (eg, key-value or KV stores) to operate entirely at …

The ever-growing amount of data requires highly scalable storage solutions. The most
flexible approach is to use storage pools that can be expanded and scaled down by adding …

The CAP theorem is a fundamental result that applies to distributed storage systems. In this
article, we first present and prove two CAP-like impossibility theorems. To state these …

Key-value stores hold the unprecedented bulk of the data produced by applications such as
social networks. Their scalability and availability requirements often outweigh sacrificing …

For frequent disk I/O and large data transmissions among different racks and physical
nodes, intermediate data communication has become the most important performance bottle …

Hybrid storage systems (HSS) use multiple different storage devices to provide high and
scalable storage capacity at high performance. Data placement across different devices is …

Exploiting the cloud storage hierarchy both within and across data-centers of different cloud
providers empowers Internet applications to choose data centers (DCs) and storage …

Increasing need for large-scale data analytics in a number of application domains has led to
a dramatic rise in the number of distributed data management systems, both parallel …

Summary form only given. Typical algorithms for decentralized data distribution work best in
a system that is fully built before it first used; adding or removing components results in …