[CS] [defense] Ding/Dissertation Defense/Nov 3, 2020

[Tricia Baclawski]

https://uchicago.zoom.us/j/4244311930?pwd=NVFEdjRsV1ZaT25sSGh6bjdpQkpxdz09

Password: 127022

       Department of Computer Science/The University of Chicago

                     *** Dissertation Defense ***


Candidate:  Yi Ding

Date:  Tuesday, November 3, 2020

Time:  10:00 AM

Place:  via zoom

Title: Learning Structure for Computer Systems Management

Abstract:
Modern computer systems expose diverse configurable parameters whose
complicated interactions have surprising effects on performance and
energy. This puts a great burden on systems designers and researchers
to manage such complexity. Machine learning (ML) creates an
opportunity to alleviate this burden by modeling resources'
complicated, non-linear interactions and deliver an optimal solution
to scheduling and resource management problems. However, naively
applying traditional ML methods, such as deep learning, creates
several challenges including generalization, robustness, and
interpretability. A lack of generalizability and robustness in the ML
models is largely due to the scarcity and bias of the training data.
Causal inference creates an opportunity to tackle these challenges by
analyzing observational data rather than data generated from
randomized experiments. Since causal inference inherently studies the
causal relationships---underlying structure---rather than correlation
between features, it also provides interpretable systems results.

This dissertation presents the algorithms and systems we developed to
improve systems outcomes by applying ML along with key techniques from
causal inference. First, we describe learning for systems optimization
with scarce data and system structure. In particular, we propose a
novel generative model to address the data scarcity issue and a
multi-phase sampling approach by exploiting system structure. Our
results show after achieving a certain level of accuracy, it is no
longer profitable for systems researchers to improve learning systems
without accounting for the structure. Thus we advocate that future
work on learning for systems should de-emphasize accuracy and instead
incorporate the system problem's structure into the learner. Second,
we describe Sherlock, a causal straggler prediction framework for
datacenters. Straggers are rare events that exhibit extreme tail
latencies, which lead to imbalance in the training data. To address
data imbalance issue, Sherlock augments correlation-based learning
with causal analysis without prior knowledge. To effectively mitigate
stragglers, Sherlock applies permutation feature importance (PFI) to
gain insights into the straggling behavior for further system
intervenation. Sherlock’s combination of PS and PFI allows it to make
accurate, interpretable predictions from imbalanced training data.
This work is evidence that causal analysis is effective in delivering
more generalizable, robust, and interpretable systems.

Yi's advisor is Prof. Henry Hoffmann

Login to the Computer Science Department website for details,
including a draft copy of the dissertation:

 https://newtraell.cs.uchicago.edu/phd/phd_announcements#dingy

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Tricia Baclawski
Student Affairs Administrator
Computer Science Department
5730 S. Ellis - Room 350
Chicago, IL 60637
pbaclawski at uchicago.edu
(773) 702-6854
/pronouns: she, her, hers/
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