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 …

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 …

The use of distributed key‐value stores (KVS) has experienced fast adoption by various
applications in recent years due to key advantages such as hypertext transfer protocol …

In recent years, edge computing has become an increasingly popular computing paradigm
to enable real-time data processing and mobile intelligence. Edge computing allows …

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 …

Many time-critical and data-intensive distributed applications for the computing continuum
depend on low-latency, scalable, and highly available distributed key value storages. In this …

Near real-time edge analytics requires dealing with the rapidly growing amount of data,
limited resources, and high failure probabilities of edge nodes. Therefore, data replication is …

As cloud services grow to span more and more globally distributed datacenters, there is an
increasingly urgent need for automated mechanisms to place application data across these …

Autonomous driving needs various line-of-sight sensors to perceive surroundings that could
be impaired under diverse environment uncertainties such as visual occlusion and extreme …

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 …