In many domains, the previous decade was characterized by increasing data volumes and
growing complexity of data analyses, creating new demands for batch processing on …

In many public and private Cloud systems, users need to specify a limit for the amount of
resources (CPU cores and RAM) to provision for their workloads. A job that exceeds its limits …

Serverless computing is becoming increasingly popular, enabling users to quickly launch
thousands of shortlived tasks in the cloud with high elasticity and fine-grain billing. These …

The proliferation of camera-enabled devices and large video repositories has led to a
diverse set of video analytics applications. These applications rely on video pipelines …

Big data frameworks such as Spark and Hadoop are widely adopted to run analytics jobs in
both research and industry. Cloud offers affordable compute resources which are easier to …

Finding good configurations of a software system is often challenging since the number of
configuration options can be large. Software engineers often make poor choices about …

The performance of compute hardware varies: software run repeatedly on the same server
(or a different server with supposedly identical parts) can produce performance results that …

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 …

With the advent of big data applications, which tend to have longer execution time, choosing
the right cloud VM has significant performance and economic implications. For example, in …

Data analytics are an important class of data-intensive workloads on public cloud services.
However, selecting the right compute and storage configuration for these applications is …