NETS 212: Scalable and Cloud Computing (Fall 2019)

Instructor Andreas Haeberlen
Location: 560 Levine Hall
Office hour: Thursdays 3-4pm
Time and location Tuesdays/Thursdays 9:00-10:30am
(Meyerson Hall B1)
Teaching assistants
Azzam Althagafi (
Office hour: Mondays 2:00-3:00pm (Levine 5th floor bump space)

  Mukund Venkateswaran (
Office hour: Mondays noon-1pm (Levine 5th floor bump space)

Ahmed Lone (
Office hour: Thursdays 6:00-7:00pm (5th floor GRW bump space)

Nicholas Ng (
Office hour: Fridays 8:00-10:00am (5th floor GRW bump space)

Alex Kempf (
Office hour: Wednesdays 9:30-10:30am (Levine 5th floor bump space)

Neil Shweky (
Office hour: Wednesdays 1:30-2:30pm (5th floor GRW bump space)

Chloe Le (
Office hour: Tuesdays 4:15-5:15pm (Levine 5th floor bump space)

Robert Epstein (
Office hour: Thursdays 5-6pm (5th floor GRW bump space)

Joe Zhou (
Office hour: Tuesdays 10:30-11:30am (5th floor GRW bump space)

Salomon Serfati (
Office hour: Thursdays 1:30-2:30pm (Levine 6th floor bump space)

Qi Linzhi (
Office hour: Fridays 2:00-3:00pm (Levine 6th floor bump space)

Zetong Jia (
Office hour: Tuesdays 11am-noon (Levine 5th floor bump space)

Matthew Kongsiri (
Office hour: Wednesdays 5:00-6:00pm (5th floor GRW bump space)
Yeon Sang Jung (
Office hour: Fridays 10:00-11:00am (5th floor GRW bump space)
Course description What is the "cloud"? How do we build software systems and components that scale to millions of users and petabytes of data, and are "always available"?

In the modern Internet, virtually all large Web services run atop multiple geographically distributed data centers: Google, Yahoo, Facebook, iTunes, Amazon, eBay, Bing, etc. Services must scale across thousands of machines, tolerate faults, and support thousands of concurrent requests. Increasingly, the major providers (including Amazon, Google, Microsoft, HP, and IBM) are looking at "hosting" third-party applications in their data centers - forming so-called "cloud computing" services. A significant number of these services also process "streaming" data: geocoding information from cell phones, tweets, streaming video, etc.

This course, aimed at a sophomore with exposure to basic programming within the context of a single machine, focuses on the issues and programming models related to such cloud and distributed data processing technologies: data partitioning, storage schemes, stream processing, and "mostly shared-nothing" parallel algorithms.

NETS212 is a required course for the NETS program and for the Data Science Minor.

Topics covered Datacenter architectures, the MapReduce programming model, Hadoop, cloud algorithms (PageRank, adsorption, friend recommendation, TF/IDF), web programming basics (servlets, AJAX, Node.js/Express, Bootstrap), higher-level programming (Hive, Pig Latin), ...
Format The format will be two 1.5-hour lectures per week, plus assigned readings. There will be regular homework assignments and a term project, plus a midterm and a final exam.
Prerequisites CIS 120, Introduction to Programming
CIS 160, Discrete Mathematics
Co-requisite: CIS 121, Data Structures
Texts and readings Hadoop: The Definitive Guide, Fourth Edition, by Tom White (O'Reilly) (ISBN 978-1-4919-0163-2; read online for free, or buy for approx. $20)
Data-Intensive Text Processing with MapReduce, by Jimmy Lin and Chris Dyer (Morgan & Claypool) (ISBN 978-1608453429; read online for free, or buy for approx. $40)
Additional materials will be provided as handouts or in the form of light technical papers.

Grading Homework 30%, Term project 30%, Exams 35%, Participation 5%
Policies You are encouraged to discuss your homework assignments with your classmates; however, any code you submit must be your own work. You may not share code with others or copy code from outside sources, except where the assignment specifically allows it. Plagiarism can have serious consequences.
Resources We will be using Piazza for course-related discussions.
Term project In three-person teams, build a small Facebook-like application using Node.js and Amazon's DynamoDB. Based on network analysis, the application should make friend recommendations; it should also visualize the social network.
Facebook award In previous years, Facebook sponsored an award for the best term project. You can learn more about the winners from previous years in the Hall of Fame.
Assignments Homework assignments will be available for download; you can submit your solution here. If necessary, you can request an extension.
Lab sessions The TAs may occasionally hold lab sessions to provide additional help with topics related to the class.
Schedule Below is the tentative schedule for the course:

Date Topic Details Reading Remarks
Aug 27 Introduction Course overview    
Aug 29 The Cloud Kinds of clouds; cloud applications
Datacenters; utility computing
Web vs. cloud vs. cluster
Armbrust et al.: A View of Cloud Computing HW0
Sep 03 Concurrency Parallel architectures; consistency models
Synchronization; locking
Deadlock and livelock; solutions
Vogels: Eventually consistent  
Sep 05 Faults and failures Internet basics; TCP and IP
Types of faults; challenges
CAP theorem; eventual consistency
Tseitlin: The Antifragile Organization HW0 due; HW1
Sep 10  
Sep 10
Course selection period ends
Sep 12 Cloud basics Introduction to Amazon Web Services
EC2 and EBS
Other services
Handout: Getting Started with AWS  
Sep 17 Cloud storage Key-value stores; concurrency control
  HW1 MS1 due
Sep 19 MapReduce Core concepts
Programming model
Examples of MapReduce algorithms
Dean and Ghemawat: MapReduce: Simplified Data Processing on Large Clusters
Lin & Dyer, Chapter 2: MapReduce Design
Sep 24
Sep 26
Programming in MapReduce Using keys to group
Different kinds of reduce functions
Shuffle implementations
White, Chapter 7: How MapReduce Works
Lin & Dyer, Chapter 3: MapReduce Algorithm Design
HW1 MS2 due; HW2
Oct 01 Hadoop Basics: Data types, drivers, mappers, reducers
HDFS; dataflow in Hadoop
Fault tolerance in Hadoop
White, Chapter 3: HDFS
White, Chapter 6: Developing a MapReduce Application
Oct 03 Graph algorithms Iterative MapReduce
Graph representations; SSSP
k-means; Naive Bayes; link analysis
Lin & Dyer, Chapter 5: Graph Algorithms  
Oct 07
Last day to drop
Oct 08 First midterm exam (covers topics through October 3rd)    
Oct 10–13
Fall break
Oct 15 Random-walk algorithms PageRank
Baluja et al.: Video suggestion and discovery for YouTube HW3
Oct 17 Iterative processing RDDs
White, Chapter 19 HW2 due
Oct 22 Web programming Client/server versus P2P
Web protocols: DNS, HTTP, ...
How to build a web server; threads vs events
Berners-Lee: Information Management: A Proposal
Google: SPDY white paper
Oct 24 HW3MS1 due
Oct 29
Andreas in Canada for SOSP — no class
Oct 31 Web services, XML, JSON Web services
Data interchange
XML; DTDs; DOM; XML schema; JSON
  HW3 MS2 due; HW4 and project handout available.
Nov 04
Last day to withdraw
Nov 05 Node.js Node.js; Express; EJS
Managing state; cookies
Web security
Getting Started with Express Form project teams
Nov 07 Dynamic content JavaScript
Google Maps
Nov 12 Security Crypto essentials
Web security
  HW4 MS1 due
Nov 14
Nov 19
Beyond MapReduce SQL
White, Chapter 12: Hive
Stonebraker et al.: MapReduce and parallel DBMSs: friends or foes?
Nov 21 Peer-to-peer P2P applications; swarming; incentives
Structured and unstructured overlays; Pastry
P2P security
Rodrigues and Druschel: Peer-to-Peer systems HW4 MS2 due (on Friday)
Nov 26 Case study: Bitcoin Distributed ledgers
Satoshi Nakamoto: Bitcoin: A Peer-to-Peer Electronic Cash System  
Nov 28
Thanksgiving break — no class
Dec 03 Case study: Facebook Storage at Facebook
Dec 05 Second midterm exam (covers all topics since the first midterm)    
Dec 10–11
Reading days
Dec 12
Finals begin; project demos
Dec 19
Finals end