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---
layout: page
title: Schedule
permalink: /schedule/
---
<table border=0 cellspacing=0 cellpadding=0 width="100%">
<tr style='background:#0f8dd8'>
<td width="15%" style='width:10%;padding:3.0pt 3.0pt 3.0pt 3.0pt'><b><span style='font-size:12.0pt;color:white'>Date</span></b></td>
<td width="10%" style='width:10%;padding:3.0pt 3.0pt 3.0pt 3.0pt'><b><span style='font-size:12.0pt;color:white'>Location</span></b></td>
<td width="15%" style='width:20%;padding:3.0pt 3.0pt 3.0pt 3.0pt'><b><span style='font-size:12.0pt;color:white'>Topic</span></b></td>
<td width="15%" style='width:15%;padding:3.0pt 3.0pt 3.0pt 3.0pt'><b><span style='font-size:12.0pt;color:white'>Notes</span></b></td>
<td width="45%" style='width:60%;padding:3.0pt 3.0pt 3.0pt 3.0pt'><b><span style='font-size:12.0pt;color:white'>Reading</span></b></td>
</tr>
<!-- Week 1 -->
<tr>
<td width="100%" colspan=5 style='width:100.0%;background:#DBE5F1;padding:3.0pt 3.0pt 3.0pt 3.0pt'><b>Week 1</b></td>
</tr>
<tr>
<td width="100%" colspan=5 style='width:100.0%;background:white;padding:5.0pt 5.0pt 5.0pt 5.0pt'><b></b></td>
</tr>
<tr style='background:white'>
<td valign=top>2018-08-28<br>15:00-17:00</td>
<td valign=top>Sal-B</td>
<td valign=top>Introduction</td>
<td valign=top>slides [<a href="/slides/01_introduction.pdf">pdf</a>]</td>
<td valign=top>The NIST Definition of Cloud Computing [<a href="/papers/2011 - The NIST Definition of Cloud Computing.pdf">pdf</a>]
<br>Above the Clouds: A Berkeley View of Cloud Computing [<a href="/papers/2009 - Above the Clouds- A Berkeley View of Cloud Computing.pdf">pdf</a>]
<br>A Comparative Taxonomy and Survey of Public Cloud Infrastructure Vendors [<a href="/papers/2018 - A Comparative Taxonomy and Survey of Public Cloud Infrastructure Vendors.pdf">pdf</a>]</td>
</tr>
<tr>
<td width="100%" colspan=5 style='width:100.0%;background:white;padding:5.0pt 5.0pt 5.0pt 5.0pt'><b></b></td>
</tr>
<tr>
<td width="100%" colspan=5 style='width:100.0%;background:white;padding:5.0pt 5.0pt 5.0pt 5.0pt'><b></b></td>
</tr>
<tr style='background:white'>
<td valign=top>2018-08-31<br>10:00-12:00</td>
<td valign=top>K-208</td>
<td valign=top>Storage<br>(GFS, Flat FS)</td>
<td valign=top>slides [<a href="/slides/02_storage_part1.pdf">pdf</a>]</td>
<td valign=top>The Google File System [<a href="/papers/2003 - The Google File System (SOSP).pdf">pdf</a>]
<br>Flat Datacenter Storage [<a href="/papers/2012 - Flat Datacenter Storage (OSDI).pdf">pdf</a>]</td>
</tr>
<tr>
<td width="100%" colspan=5 style='width:100.0%;background:white;padding:5.0pt 5.0pt 5.0pt 5.0pt'><b></b></td>
</tr>
<!-- Week 2 -->
<tr>
<td width="100%" colspan=5 style='width:100.0%;background:#DBE5F1;padding:3.0pt 3.0pt 3.0pt 3.0pt'><b>Week 2</b></td>
</tr>
<tr style='background:white'>
<td valign=top>2018-09-03<br>15:00-17:00</td>
<td valign=top>Sal-B</td>
<td valign=top>Storage<br>(Dyanmo, BigTable, Cassandra)</td>
<td valign=top>slides [<a href="/slides/03_storage_part2.pdf">pdf</a>]</td>
<td valign=top>Dynamo: Amazon's Highly Available Key-value Store [<a href="/papers/2007 - Dynamo - Amazons Highly Available Key-value Store (SOSP).pdf">pdf</a>]
<br>Bigtable: A Distributed Storage System for Structured Data [<a href="/papers/2006 - Bigtable - A Distributed Storage System for Structured Data (OSDI).pdf">pdf</a>]
<br>Cassandra: A Decentralized Structured Storage System [<a href="/papers/2010 - Cassandra - A Decentralized Structured Storage System (SIGOPS).pdf">pdf</a>]</td>
</tr>
<tr>
<td width="100%" colspan=5 style='width:100.0%;background:white;padding:5.0pt 5.0pt 5.0pt 5.0pt'><b></b></td>
</tr>
<!-- Week 3 -->
<tr>
<td width="100%" colspan=5 style='width:100.0%;background:#DBE5F1;padding:3.0pt 3.0pt 3.0pt 3.0pt'><b>Week 3</b></td>
</tr>
<tr style='background:white'>
<td valign=top>2018-09-10<br>13:00-15:00</td>
<td valign=top>Sal-C</td>
<td valign=top>Programming Languages<br>(Scala)</td>
<td valign=top>slides [<a href="/slides/04_scala.pdf">pdf</a>]</td>
<td valign=top>Scala By Example [<a href="/papers/Scala By Example.pdf">pdf</a>]</td>
</tr>
<tr>
<td width="100%" colspan=5 style='width:100.0%;background:white;padding:5.0pt 5.0pt 5.0pt 5.0pt'><b></b></td>
</tr>
<tr style='background:white'>
<td valign=top>2018-09-14<br>10:00-12:00</td>
<td valign=top>K-208</td>
<td valign=top>Parallel Data Processing<br>(MapReduce, FlumeJava)</td>
<td valign=top>slides [<a href="/slides/05_parallel_processing_part1.pdf">pdf</a>]</td>
<td valign=top>MapReduce Simplifed Data Processing on Large Clusters [<a href="/papers/2004 - MapReduce Simplifed Data Processing on Large Clusters (OSDI).pdf">pdf</a>]
<br>FlumeJava: Easy, Efficient Data-Parallel Pipelines [<a href="/papers/2010 - FlumeJava - Easy, Efficient Data-Parallel Pipelines (PLDI).pdf">pdf</a>]
<br>Data-Intensive Text Processing with MapReduce (Ch. 2-3)</td>
</tr>
<tr>
<td width="100%" colspan=5 style='width:100.0%;background:white;padding:5.0pt 5.0pt 5.0pt 5.0pt'><b></b></td>
</tr>
<!-- Week 4 -->
<tr>
<td width="100%" colspan=5 style='width:100.0%;background:#DBE5F1;padding:3.0pt 3.0pt 3.0pt 3.0pt'><b>Week 4</b></td>
</tr>
<tr style='background:white'>
<td valign=top>2018-09-18<br>10:00-12:00</td>
<td valign=top>Sal-C</td>
<td valign=top>Parallel Data Processing<br>(Spark)</td>
<td valign=top>slides [<a href="/slides/06_parallel_processing_part2.pdf">pdf</a>]</td>
<td valign=top>Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing [<a href="/papers/2012 - Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing (NSDI).pdf">pdf</a>]
<br>Spark - The Definitive Guide (Ch. 2, 12-14)</td>
</tr>
<tr>
<td width="100%" colspan=5 style='width:100.0%;background:white;padding:5.0pt 5.0pt 5.0pt 5.0pt'><b></b></td>
</tr>
<!-- Week 5 -->
<tr>
<td width="100%" colspan=5 style='width:100.0%;background:#DBE5F1;padding:3.0pt 3.0pt 3.0pt 3.0pt'><b>Week 5</b></td>
</tr>
<tr style='background:white'>
<td valign=top>2018-09-24<br>15:00-17:00</td>
<td valign=top>Sal-C</td>
<td valign=top>Staructured Data Processing<br>(Spark SQL)</td>
<td valign=top>slides [<a href="/slides/07_structured_data_processing.pdf">pdf</a>] [<a href="/slides/people.json">data</a>]</td>
<td valign=top>Spark SQL: Relational Data Processing in Spark [<a href="/papers/2015 - Spark SQL - Relational Data Processing in Spark (SIGMOD).pdf">pdf</a>]
<br>Spark - The Definitive Guide (Ch. 4-11)</td>
</td>
</tr>
<tr>
<td width="100%" colspan=5 style='width:100.0%;background:white;padding:5.0pt 5.0pt 5.0pt 5.0pt'><b></b></td>
</tr>
<tr>
<td width="100%" colspan=5 style='width:100.0%;background:white;padding:5.0pt 5.0pt 5.0pt 5.0pt'><b></b></td>
</tr>
<tr style='background:white'>
<td valign=top>2018-09-27<br>10:00-12:00</td>
<td valign=top>Sal-B</td>
<td valign=top>Stream Processing<br>(Introduction, Kafka)</td>
<td valign=top>slides [<a href="/slides/08_stream_processing_part1.pdf">pdf</a>]</td>
<td valign=top>High-Availability Algorithms for Distributed Stream Processing [<a href="/papers/2005 - High-Availability Algorithms for Distributed Stream Processing (ICDE).pdf">pdf</a>]
<br>Kafka: a Distributed Messaging System for Log Processing [<a href="/papers/2011 - Kafka: a Distributed Messaging System for Log Processing (NetDB).pdf">pdf</a>]
<br>Designing Data-Intensive Applications (Ch. 11)
<br>Fundametals of Stream Processing (Ch. 1-5, 7, 9)<td>
</tr>
<tr>
<td width="100%" colspan=5 style='width:100.0%;background:white;padding:5.0pt 5.0pt 5.0pt 5.0pt'><b></b></td>
</tr>
<!-- Week 6 -->
<tr>
<td width="100%" colspan=5 style='width:100.0%;background:#DBE5F1;padding:3.0pt 3.0pt 3.0pt 3.0pt'><b>Week 6</b></td>
</tr>
<tr style='background:white'>
<td valign=top>2018-10-01<br>13:00-15:00</td>
<td valign=top>Sal-C</td>
<td valign=top>Stream Processing<br>(Storm, Millwheel, Google Dataflow)</td>
<td valign=top>slides [<a href="/slides/09_stream_processing_part2.pdf">pdf</a>] [<a href="/slides/storm-wordcount.zip">src</a>]</td>
<td valign=top>Storm @Twitter [<a href="/papers/2014 - Storm @Twitter (SIGMOD).pdf">pdf</a>]
<br>MillWheel: Fault-Tolerant Stream Processing at Internet Scale [<a href="/papers/2013 - MillWheel - Fault-Tolerant Stream Processing at Internet Scale (VLDB).pdf">pdf</a>]
<br>The Dataflow Model: A Practical Approach to Balancing Correctness, Latency, and Cost in Massive-Scale, Unbounded, Out-of-Order Data Processing [<a href="/papers/2015 - The Dataflow Model (VLDB).pdf">pdf</a>] </td>
</tr>
<tr>
<td width="100%" colspan=5 style='width:100.0%;background:white;padding:5.0pt 5.0pt 5.0pt 5.0pt'><b></b></td>
</tr>
<tr style='background:white'>
<td valign=top>2018-10-05<br>13:00-15:00</td>
<td valign=top>Sal-B</td>
<td valign=top>Stream Processing<br>(Spark Streaming, Flink)</td>
<td valign=top>slides [<a href="/slides/10_stream_processing_part3.pdf">pdf</a>] [<a href="/slides/ss-wordcount.zip">src</a>]</td>
<td valign=top>Discretized Streams: An Efficient and Fault-Tolerant Model for Stream Processing on Large Clusters [<a href="/papers/2013 - Discretized Streams: Fault-Tolerant Streaming Computation at Scale (SOSP).pdf">pdf</a>]
<br>Apache Flink: Stream and Batch Processing in a Single Engine [<a href="/papers/2015 - Apache Flink: Stream and Batch Processing in a Single Engine.pdf">pdf</a>]
<br>Spark - The Definitive Guide (Ch. 20-23)</td>
</tr>
<tr>
<td width="100%" colspan=5 style='width:100.0%;background:white;padding:5.0pt 5.0pt 5.0pt 5.0pt'><b></b></td>
</tr>
<!-- Week 7 -->
<tr>
<td width="100%" colspan=5 style='width:100.0%;background:#DBE5F1;padding:3.0pt 3.0pt 3.0pt 3.0pt'><b>Week 7</b></td>
</tr>
<tr style='background:white'>
<td valign=top>2018-10-08<br>15:00-17:00</td>
<td valign=top>Sal-B</td>
<td valign=top>Graph Processing<br>(Pregel, GraphLab, X-Stream)</td>
<td valign=top>slides [<a href="/slides/11_graph_processing_part1.pdf">pdf</a>]</td>
<td valign=top>Pregel: A System for Large-Scale Graph Processing [<a href="/papers/2010 - Pregel - A System for Large-Scale Graph Processing (SIGMOD).pdf">pdf</a>]
<br>Distributed GraphLab: A Framework for Machine Learning and Data Mining in the Cloud [<a href="/papers/2012 - Distributed GraphLab: A Framework for Machine Learning and Data Mining in the Cloud (VLDB).pdf">pdf</a>]<br>PowerGraph: Distributed Graph-Parallel Computation on Natural Graphs [<a href="/papers/2012 - PowerGraph - Distributed Graph-Parallel Computation on Natural Graphs (OSDI).pdf">pdf</a>]
<br>X-Stream: Edge-Centric Graph Processing using Streaming Partitions [<a href="/papers/2013 - X-Stream - Edge-Centric Graph Processing using Streaming Partitions (SOSP).pdf">pdf</a>]</td>
</tr>
<tr>
<td width="100%" colspan=5 style='width:100.0%;background:white;padding:5.0pt 5.0pt 5.0pt 5.0pt'><b></b></td>
</tr>
<tr style='background:white'>
<td valign=top>2018-10-09<br>10:00-12:00</td>
<td valign=top>Sal-A</td>
<td valign=top>Graph Processing<br>(GraphX, Giraph++, Pegasus)</td>
<td valign=top>slides [<a href="/slides/12_graph_processing_part2.pdf">pdf</a>]</td>
<td valign=top>GraphX: Graph Processing in a Distributed Dataflow Framework [<a href="/papers/2014 - GraphX - Graph Processing in a Distributed Dataflow Framework (OSDI).pdf">pdf</a>]
<br>From "Think Like a Vertex" to "Think Like a Graph" [<a href="/papers/2013 - From Think Like a Vertex to Think Like a Graph (VLDB).pdf">pdf</a>]
<br>Pegasus: Mining Peta-Scale Graphs [<a href="/papers/2010 - Pegasus: Mining Peta-Scale Graphs (KAIS).pdf">pdf</a>]
<br>Spark - The Definitive Guide (Ch. 30)</td>
</tr>
<tr>
<td width="100%" colspan=5 style='width:100.0%;background:white;padding:5.0pt 5.0pt 5.0pt 5.0pt'><b></b></td>
</tr>
<tr style='background:white'>
<td valign=top>2018-10-12<br>10:00-12:00</td>
<td valign=top>Sal-C</td>
<td valign=top>Resource Management<br>(Mesos, YARN)</td>
<td valign=top>slides [<a href="/slides/13_resource_management.pdf">pdf</a>]</td>
<td valign=top>Mesos: A Platform for Fine-Grained Resource Sharing in the Data Center [<a href="/papers/2011 - Mesos - A Platform for Fine-Grained Resource Sharing in the Data Center (NSDI).pdf">pdf</a>]
<br>Apache Hadoop YARN: Yet Another Resource Negotiator [<a href="/papers/2013 - Apache Hadoop YARN - Yet Another Resource Negotiator (SoCC).pdf">pdf</a>]</td>
</tr>
<tr>
<td width="100%" colspan=5 style='width:100.0%;background:white;padding:5.0pt 5.0pt 5.0pt 5.0pt'><b></b></td>
</tr>
</table>