Knob tuning plays an important role in database optimization, which tunes knob settings to
optimize the database performance or improve resource utilization. However, there are …

Modern database management systems (DBMS) expose hundreds of configurable knobs to
control system behaviours. Determining the appropriate values for these knobs to improve …

Large-scale cloud providers rely on cluster managers for container allocation and load
balancing (eg, Kubernetes), VM provisioning (eg, Protean), and other management tasks …

Database management systems (DBMSs) are notoriously difficult to deploy and administer.
Self-driving DBMSs seek to remove these impediments by managing themselves …

Database and big data analytics systems such as Hadoop and Spark have a large number
of configuration parameters that control memory distribution, I/O optimization, parallelism …

Configuration tuning is vital to optimize the performance of big data analysis platforms like
Spark. Existing methods (eg auto-tuning relational databases) are not effective for tuning …

The increasing scale and complexity of workloads in modern cloud services highlight a
crucial challenge in automated index tuning: recommending high-quality indexes while …

UDO is a versatile tool for offline tuning of database systems for specific workloads. UDO
can consider a variety of tuning choices, reaching from picking transaction code variants …

The distributed data analytic system--Spark is a common choice for processing massive
volumes of heterogeneous data, while it is challenging to tune its parameters to achieve …

The microservice architecture allows applications to be designed in a modular format,
whereby each microservice can implement a single functionality and can be independently …