DISSERTATION DEFENSE

Author : Md Shahriar Iqbal

Advisor : Dr. Pooyan Jamshidi

Date : November 10, 2023

Time:  9 am - 10:30 am

Place : Innovation Building, Room 2267 & Virtual

Meeting Link : Topic: Shahriar Iqbal's PhD Thesis Defense

Time: Nov 10, 2023 09:00 AM Eastern Time (US and Canada)

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                                                                                                               https://us02web.zoom.us/j/84549470782?pwd=TmpHaG44NVVMT0FLb3N1SmFZWWZBQ…                                                                                   
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                                                                                                                                                  One tap mobile                                                                                                                                           
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Passcode: 304119

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Abstract

Modern computer systems are highly configurable, with hundreds of configuration options that interact, resulting in an enormous configuration space. Understanding and reasoning about the performance behavior of highly configurable systems is challenging. This is further worsened due to several system constraints such as large configuration evaluation time, noisy measurements, limited experimentation budget, and accessibility, restricting the capacity to troubleshoot or optimize highly configurable systems. As a result, the significant performance potential already built in many of our modern computer systems remains untapped. Unfortunately, manual configuring is labor-intensive, time-consuming, and often infeasible, even for domain experts. Recently, several search-based and learning-based automatic configuration approaches have been proposed to overcome these issues; nevertheless, critical challenges still remain as they (i) are unaware of the variations of evaluation times between certain performance goals, (ii) may produce incorrect explanation and, (iii) become unreliable in unseen environments (e.g., different hardware, workloads). The primary goal of this thesis is to overcome the aforementioned limitations by adopting a data-driven strategy to design performance debugging and optimization approaches tools that are efficient, scalable, and can be reliably used by developers across different deployment scenarios. We developed a novel cost-aware acquisition function for multi-objective optimization technique, called FlexiBO, that solves the sub-optimality for resource constrained devices. Instead of evaluating all objective functions, our optimization approach chooses the one for evaluation that has the potential to provide the maximum benefit weighted by the objective evaluation cost. Later, we also developed a performance debugging technique, known as Unicorn, which captures intricate interactions between configuration options across the software-hardware stack and describes how such interactions can impact performance variations via causal inference. Finally, we proposed CAMEO - a method that identifies invariant causal predictors under deployment environmental changes, allowing the optimization process to operate in a reduced search space, leading to faster optimization of system performance. We showed the promise of our debugging and optimization techniques through extensive and thorough evaluation on a wide range of software systems over a large design space.