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EECS 598: Big Data Systems and Applications


  • Catalog Number: 31878
  • Lectures/Discussion: 2166 DOW, MW: 12:00 PM – 1:30 PM
  • Projects/Makeup: 1690 BEYSTER, F 12:00 PM – 1:00 PM


Member (uniqname) Role Office Hours
Mosharaf Chowdhury (mosharaf) Faculty 4820 BBB. By appointments only.
Yiwen Zhang (yiwenzhg) GSI BBB Learning Center, F 2:00 PM - 4:00 PM


ALL communication regarding this course must be via Piazza. This includes questions, discussions, announcements, as well as private messages.

Presentation slides and paper summaries should be emailed to

Course Description

This class will introduce you to the key concepts and the state-of-the-art in big data systems and encourage you to think about either building new tools or how to apply an existing one in your own research.

Since datacenters and cloud computing form the backbone of modern big data systems, we will start with high-level overviews of the two and discuss emerging trends in both software and hardware. We will then take a deep dive into the big data systems landscape, focusing on different types of problems. Our journey will cover topics from top conferences such as SOSP, OSDI, NSDI, SIGCOMM, SIGMOD, and EuroSys on SQL queries, log analysis, machine learning activities, graph processing, approximation queries, stream processing, interactive analysis, and deep learning.


Students are expected to have good programming skills and must have taken at least one undergraduate-level systems-related course (from operating systems, databases, distributed systems, and networking).

Undergraduates must receive explicit permission from the instructor to enroll, if space permits.


This course has no textbooks. We will read recent papers from top venues to understand trends in big data systems and their applications.

Tentative Schedule and Reading List

  • Mandatory: Unless otherwise specified.
  • Optional: Can skip, but should skim.
  • Companion: Mandatory for presenters and critics. Optional for the rest.
Date Readings Presenter Critic
Sep 6 Introduction Mosharaf
Sep 11 The Datacenter as a Computer (Chapters 1 and 2) Mosharaf
VL2: A Scalable and Flexible Data Center Network (Optional)
Sep 13 The Google File System Mosharaf
MapReduce: Simplified Data Processing on Large Clusters
GFS: Evolution on Fast-Forward (Optional)
Resource Management
Sep 18 YARN: Yet Another Resource Negotiator Matthew-Ayush-HyunJong ChunJung-TingWei-Vandit*
Borg, Omega, and Kubernetes (Companion)
Mesos: A Platform for Fine-Grained Resource Sharing in the Data Center (Optional)
Sep 20 Dominant Resource Fairness: Fair Allocation of Multiple Resource Types Fan-Hasan-Henry Andrew-William-Zhao*
Altruistic Scheduling in Multi-Resource Clusters (Companion)
Dataflow Programming Frameworks and Execution Engines
Sep 27 Resilient Distributed Datasets: A Fault-tolerant Abstraction for In-memory Cluster Computing Dong-Jinxiaoyu-Huanyu Qiyang-Ruying
Apache Tez: A Unifying Framework for Modeling and Building Data Processing Applications (Companion)
Sep 29 Naiad: A Timely Dataflow System Die-Chi-Shaowen Matthew-Ayush-HyunJong
Batch Processing
Oct 2 Spark SQL: Relational Data Processing in Spark Bor-ChungWen-Hongyu Dong-Jinxiaoyu-Huanyu
Major Technical Advancements in Apache Hive (Companion)
Oct 4 Global Analytics in the Face of Bandwidth and Regulatory Constraints Fan-Hasan-Henry* Boyu-Rui-Haojun*
Clarinet: WAN-Aware Optimization for Analytics Queries (Companion)
Stream Processing
Oct 9 Discretized Streams: Fault-Tolerant Streaming Computation at Scale ChunJung-TingWei-Vandit TaiYing-PeiXuan-Changfeng
Storm @Twitter (Companion)
Oct 11 Realtime Data Processing at Facebook TaiYing-PeiXuan-Changfeng Wen-Eric-Kevin
Twitter Heron: Stream Processing at Scale (Companion)
Oct 18 StreamScope: Continuous Reliable Distributed Processing of Big Data Streams Die-Chi-Shaowen* Bor-ChungWen-Hongyu
Graph Processing
Oct 23 PowerGraph: Distributed Graph-Parallel Computation on Natural Graphs Wen-Eric-Kevin* Dong-Jinxiaoyu-Huanyu*
GraphX: Graph Processing in a Distributed Dataflow Framework (Companion)
Machine Learning
Oct 25 Scaling Distributed Machine Learning with the Parameter Server Qiyang-Ruying Die-Chi-Shaowen
Project Adam: Building an Efficient and Scalable Deep Learning Training System (Optional)
Oct 30 TensorFlow: A System for Large-Scale Machine Learning Wen-Eric-Kevin Boyu-Rui-Haojun
Nov 1 TuX2: Distributed Graph Computation for Machine Learning Andrew-William-Zhao Wenting-Peter
Nov 10 Gaia: Geo-Distributed Machine Learning Approaching LAN Speeds Matthew-Ayush-HyunJong* Fan-Hasan-Henry
Nov 13 Mid-Semester Presentations
Approximate Query Processing
Nov 17 BlinkDB: Queries with Bounded Errors and Bounded Response Times on Very Large Data Boyu-Rui-Haojun Wenting-Peter*
Nov 22 Quickr: Lazily Approximating Complex AdHoc Queries in BigData Clusters Andrew-William-Zhao Bor-ChungWen-Hongyu*
RDMA-Enabled Systems
Nov 29 FaRM: Fast Remote Memory Qiyang-Ruying* Yiwen** TaiYing-PeiXuan-Changfeng*
No Compromises: Distributed Transactions with Consistency, Availability, and Performance (Companion)
Dec 4 Efficient Memory Disaggregation with Infiniswap Wenting-Peter ChunJung-TingWei-Vandit
Dec 6 Wrap Up Mosharaf
Dec 11 Final Presentations


Assignment Track Research Track
Paper Summary 20% 20%
Paper Presentation 20% 20%
Participation 10% 10%
Assignment 1 15% -
Assignment 2 15% -
Assignment 3 20% -
Research Proposal - 10%
Mid-Semester Checkpoint - 15%
Final Report - 25%


Honor Code

The Engineering Honor Code applies to all activities related to this course.


All activities of this course will be performed in groups of 3 students for the Assignment track. Exceptions may be made for the Research track.

Declare your group's membership and paper preferences by September 11, 2017. After this date, we will form groups from the remaining students.

Paper Presentation

The course will be conducted as a seminar. Only one group will present in each class. Each group will be assigned to present a paper at least once throughout the semester. Presentations should last at most 45 minutes without interruption. However, presenters should expect questions and interruptions throughout. In the presentation, you should:

  • Motivate the paper and provide background.
  • Present the high level idea, approach, and/or insight (using examples, whenever appropriate).
  • Discuss technical details so that one can understand the key details without carefully reading it.
  • Explain the difference between this paper and related work.
  • Raise questions throughout the presentation to generate discussion.

The slides for a presentation must be emailed to the instructor team at least 24 hours prior to the corresponding class. You should use this template for making your slides in powerpoint.

Paper Summaries

Each group will also be assigned to write one or more paper summaries. The paper summary assigned to a group may not be the same paper they have presented.

A paper summary must address the following four questions in sufficient details (2-3 pages):

  • What is the problem addressed by the paper, and why is this problem important?
  • What is the hypothesis of the work?
  • What is the proposed solution, and what key insight guides their solution?
  • What is one (or more) drawback or limitation of the proposal, and how will you improve it?

The paper summary of a paper must be emailed to the instructor team within 24 hours after its presentation. Late reviews will not be counted. You should use this template for writing your summary. Allocate enough time for your reading, discuss as a group, write the summary carefully, and finally, include key observations from the class discussion.

Because you do not have to write summaries/reviews for each paper, you cannot avoid reading a paper. Everyone is expected to have read all the papers. Being able to critically judge others' work is crucial for your understanding.


You are expected to attend all lectures (you may skip up to 2 lectures due to legitimate reasons), and more importantly, participate in class discussions.

Assignment Track

If your group chooses the assignment track, you will have to complete two assignments on popular big data frameworks Apache Spark and TensorFlow. The third assignment will likely be different for each group with specific goals, and this will resemble a small research exploration project. Details of the assignments will be available over time. Tentative deadlines for the assignments are October 11, November 13, and December 11.

Research Track

If your group chooses the research track, you will have to complete substantive work an instructor-approved problem and have original contribution. Surveys are not permitted as projects; instead, each project must contain a survey of background and related work. You must meet the following milestones (unless otherwise specified in future announcements) to ensure a high-quality project at the end of the semester:

  • Turn in a 2-page draft proposal (including references) by September 27. Remember to include the names and Michigan email addresses of the group members.
  • Keep revising your initial idea and incorporate instructor feedback. However, your team and project proposal must be finalized and approved on or before October 11.
  • Each group must submit a 4-page mid-semester progress report and present mid-semester progress during class hours on the week of November 13.
  • Each group must present their final results during a presentation or poster session on December 11.
  • Each group must turn in an 8-page final report and your code via email on or before 11:59PM EST on December 15. The report must be submitted as a PDF file, with formatting similar to that of the papers you've read in the class. The self-contained (i.e., include ALL dependencies) code must be submitted as a zip file. Each zip file containing the code must include a README file with a step-by-step guide on how to compile and run the provided code.