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 …
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 …