To increase utilization, datacenter schedulers often overcommit resources where the sum of
resources allocated to the tasks on a machine exceeds its physical capacity. Setting the right …

Cloud databases have been widely accepted and deployed due to their unique advantages,
such as high elasticity, high availability, and low cost. Many new techniques, such as …

Python has become overwhelmingly popular for ad-hoc data analysis, and Pandas
dataframes have quickly become the de facto standard API for data science. However …

Robust forecasts of future resource usage in cloud computing environments enable high
efficiency in resource management solutions, such as autoscaling and overcommitment …

Existing machine learning (ML) approaches to automatically optimize database
management systems (DBMSs) only target a single configuration space at a time (eg, knobs …

Web companies typically run latency-critical long-running services and resource-intensive,
throughput-hungry batch jobs in a shared cluster for improved utilization and reduced cost …

In this tutorial, we review advances made recently in a decades-old problem, namely query
optimization. Along with the traditional optimization techniques many of which are still being …

To efficiently store and query a DBMS, administrators must select storage and indexing
configurations. For example, one must decide whether data should be stored in rows or …

Storage, as a core function and fundamental component of computers, provides services for
saving and reading digital data. The increasing complexity of data operations and storage …

Relational cloud Database-as-a-Service offerings run on multi-tenant infrastructure
consisting of clusters of nodes, with each node hosting multiple tenant databases. Such …