Customized OS support for data processing on modern hardware

For decades, data processing systems have found the generic interfaces and policies offered by the operating systems at odds with the need for efficient utilization of hardware resources. As a result, most engines circumvent the OS and manage hardware resources directly. With the growing complexity and heterogeneity of modern machines, data processing engines are now facing a steep increase in the complexity they must absorb to achieve good performance.

In this talk we will focus on the challege of running concurrent workloads in multi-programming execution environments, as systems' performance often suffers from resource interaction among multiple parallel jobs. In the light of recent advancements in operating system design, such as multi-kernels, we propose two key principles: the separation of compute and control planes on a multi-core machine, and customization of the compute plane as a light weight OS kernel tailored for data processing. I will present some of our design decisions, and how they help to improve the performance of workloads consisting of common graph algorithms and relational operators.

Short Bio:

Jana Giceva is a final year PhD student in the Systems Group at ETH Zurich, supervised by Gustavo Alonso, and co-advised by Timothy Roscoe. Her research interests revolve around systems running on modern hardware, with inclination towards engines for in-memory data processing and operating systems. During her PhD studies she has been exploring various cross-layer optimizations across the systems stack, touching aspects from both hardware/software and database/OS co-design. Some of these projects are part of industry collaboration with Oracle Labs. She received the European Google PhD Fellowship 2014 in Operating Systems.