Fall 2025
Instructor: Qizhen Zhang
Time: Tuesday 9:00 AM - 11:00 AM
Location: BA 2195
Discussions: Piazza
Office hours: Tuesday 11:00 AM - 12:00 PM or by appointment in BA 7202
Course Description
A big move in the development of database management systems in recent years is the migration to the cloud,
where users enjoy a variety of benefits, e.g., high availability, scalability, and elasticity, with database as a service.
Meanwhile, database system architectures must be adapted to evolving trends in the cloud environment.
In this seminar course, we will discuss state-of-the-art cloud database systems recently published in top-tier database venues.
Topics include resource (storage and memory) disaggregation, new interconnect technologies (e.g., RDMA and CXL), serverless, multi-tenancy, and, more recently, vector DB and LLMs.
There will also be a final project where groups of students research a specific aspect of cloud databases.
Prerequisites
General background in database systems, computer systems, and networking (e.g., CSC343, CSC458, and CSC2209 or equivalent courses) is recommended.
Format
This seminar course consists of paper discussions and a final project.
You need to form teams of 2 students for the project.
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Paper discussions.
We meet for two hours every Tuesday to discuss two papers on a particular topic (see the schedule below).
Before the class, students need to read both papers but submit the review (i.e., summary, strengths, weaknesses, and detailed comments) for the first paper only on HotCrp.
During the class, all students are expected to participate in the discussion.
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The first half of each class starts with a short lecture on the topic and the second paper, which ends with a list of (most likely open) questions.
Our discussion centers around these questions.
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The second half of the class focuses on the more recent paper.
To drive the discussion, it is presented by two students: an advocator followed by an adversary.
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The student who serves as the advocator introduces the work in the first 15 min, followed by 15 min discussing its significance (e.g., strong motivation, innovative techniques, or the high impact it has generated or will generate) and addressing potential concerns.
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The other student spends the next 20 min criticizing the work by showing, for example, the unrealistic assumptions, potential deployment issues, or limited impact.
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Other students can join the discussion anytime (which is counted as your class participation) and will vote for a side at the end of the session.
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If our schedule is perfect, each student serves one time as the advocator and one time as the adversary.
However, this is unlikely.
To incentivize leading discussions, a student who leads discussion one time can skip the reviews of two papers (including the one presented).
They also get the full participation marks for the papers presented.
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The project.
The second part of the course is to execute a research project on any topic related to cloud data systems.
Each team (2 students) will submit a 1-page project proposal (including necessary references) that will be reviewed and approved by the instructor.
At the end of the term, each team will give a 15-minute presentation (12-minute talk + 3-minute Q&A) and write a final report.
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The project can be either the reproduction of a recently published work (or multiple of them) or the development of original ideas.
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In the former case, students should understand, implement, and eventually improve the ideas of the work.
Project quality will thus be evaluated by comprehension (20% if the selected work is clearly presented), completion (60% if the core ideas of the selected work are implemented and evaluated), and innovation (20% if new ideas are proposed, implemented, and evaluated).
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If a team decide to focus on their own ideas, project quality will be evaluated entirely by innovation.
In that case, students need to justify the novelty by elaborating the design and comparing it to prior work (50%) and demonstrate the feasibility by implementing and evaluating the ideas (50%).
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The project proposal should clearly state which approach the team pursues.
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Experimentation platform: Chameleon (instructions will be provided later).