A framework for intelligence augmented computing systems

Large-scale computing systems rely on many control and decision-making algorithms. Classical approaches to designing and optimizing these algorithms are poorly suited to the diverse and demanding requirements of modern systems and emerging applications. The state of the art paradigm for building these control algorithms often devolves into painstakingly built, handcrafted, average-case heuristics. However, as systems and applications have grown in complexity and heterogeneity, designing fixed algorithms that work well across a variety of conditions has become exceedingly difficult and costly. Moreover, we are reaching the limits of conventional approaches of generating heuristics, which involve recurring human-expert-driven engineering efforts. Such an approach will be untenable in the future. In this thesis, we investigate a new paradigm for solving large scale system management and optimization problems. We develop systems that can learn to optimize the performance on their own using modern machine learning techniques. As a result, in the proposed approach, the system designer need not develop specialized heuristics for low-level design goals. Instead, the designer architects a framework for measurement, estimation, experimentation, and learning that discovers the low-level actions that achieve high-level resource management objectives automatically. We use this approach to build a series of practical intelligent controllers for the management and optimization of large-scale data-parallel and data-processing workloads on heterogeneous computer systems. Our contributions encompass building mathematical models (e.g., for denoising telemetry data), policies (e.g., for scheduling), optimizations to enable real time inference, and the design and implementation of practical software and hardware that provides efficient, scalable, and composable system management solutions.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2022

Author dc:creator

• Banerjee, Subho Sankar

Contributors dc:contributor

• Iyer, Ravishankar K
• Hwu, Wen-mei
• Adve, Vikram S
• Mitra, Subhasish

• Machine Learning for Systems
• Reinforcement learning
• Bayesian Methods
• Accelerators
• Scheduling
• Performance Monitoring
• Error Correction

Statement dc:rights

• Copyright 2022 Subho Banerjee

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/115749

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F115749