Enterprises are moving their business-critical workloads to public clouds at an accelerating
pace. Multi-tenancy is a crucial tenet for cloud data service providers allowing them to …

Elasticity of compute and storage is crucial for analytical cloud database systems. All cloud
vendors provide disaggregated object stores, which can be used as storage backend for …

Deep learning training on cloud platforms usually follows the tradition of the separation of
storage and computing. The training executes on a compute cluster equipped with …

Machine learning (ML) has proven itself in high-value web applications such as search
ranking and is emerging as a powerful tool in a much broader range of enterprise scenarios …

Database administrators (DBAs) were traditionally responsible for optimizing the on-premise
database workloads. However, with the rise of cloud data services, where cloud providers …

Active storage devices and in-storage computing are proposed and developed in recent
years to effectively reduce the amount of required data traffic and to improve the overall …

Scientific research and development campaigns are materialized by workflows of
applications executing on high-performance computing (HPC) systems. These applications …

Modern data analytics frameworks often integrate with external storage services, which can
lead to storage bottlenecks. Existing caching and prefetching solutions utilize high-level …

The Hadoop distributed file system (HDFS) is responsible for storing very large data-sets
reliably on clusters of commodity machines. The HDFS takes advantage of replication to …

Kariz is a new architecture for caching data from datalakes accessed, potentially
concurrently, by multiple analytic platforms. It integrates rich information from analytics …