Recent workload trends indicate rapid growth in the deployment of machine learning,
genomics and scientific workloads on cloud computing infrastructure. However, efficiently …

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 …

Users of cloud services are presented with a bewildering choice of VM types and the choice
of VM can have significant implications on performance and cost. In this paper we address …

Modern deep learning frameworks support a variety of hardware, including CPU, GPU, and
other accelerators, to perform computation. In this paper, we study how to schedule jobs …

In this paper we discuss advances in self-tuning database systems over the past decade,
based on our experience in the AutoAdmin project at Microsoft Research. This paper …

One of the most challenging aspects of managing a very large data warehouse is identifying
how queries will behave before they start executing. Yet knowing their performance …

Predicting query execution time is useful in many database management issues including
admission control, query scheduling, progress monitoring, and system sizing. Recently the …

Accurate query performance prediction (QPP) is central to effective resource management,
query optimization and query scheduling. Analytical cost models, used in current generation …

Query processing over big data is ubiquitous in modern clouds, where the system takes care
of picking both the physical query execution plans and the resources needed to run those …

Traditional query optimizers rely on the accuracy of estimated statistics to choose good
execution plans. This design often leads to suboptimal plan choices for complex queries …