ICSA Colloquium - 15/07/2021

Title: Optimising Computer Systems in High Dimensional and Complex Parameter Space

Abstract: Performance tuning of computer systems is challenging for a variety of reasons. Modern computer systems expose many configuration parameters in a complex and massive parameter space. The systems are nonlinear and there is no method for quantifying or modelling such systems by performance tuning to the level of precision required. Furthermore, scheduling of tasks or resource allocation may require the control of dynamically evolving tasks. Auto-tuning has emerged using a black-box optimiser such as Bayesian Optimisation (BO). However, BO has limited scalability. Reinforcement Learning (RL) could be applied for combinatorial optimisation problems, but there is a gap between current research and practical RL deployments. I will introduce our framework to tackle these issues and demonstrate the potential of machine learning based methodologies for computer system optimisation.  

Bio: Eiko Yoneki is a senior researcher in the Systems Research Group of the University of Cambridge Computer Laboratory and a Turing Fellow at the Alan Turing Institute. Her research interests span distributed systems, networking and databases, including large-scale graph processing. Her group’s current research focusses on auto-tuning of data processing/analytics framework to deal with complex parameter space using ML.