This document provides an introduction to a course on cloud computing and data intensive computing. The course objectives are to introduce key concepts and principles of cloud computing and data intensive computing, and to teach how to read, review, and present scientific papers. Topics covered in the course include cloud platforms, databases, resource management, and data processing frameworks. Students will complete reading assignments, exams, a presentation, and a final project.

1. Introduction
Amir H. Payberah
amir@sics.se
Amirkabir University of Technology
(Tehran Polytechnic)
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2. Course Information
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3. Course Objective
Introduction to main concepts and principles of cloud computing
and data intensive computing.
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4. Course Objective
Introduction to main concepts and principles of cloud computing
and data intensive computing.
How to read, review and present a scientific paper.
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5. Topics of Study
Topics we will cover include:
• Cloud platforms
• Cloud storage, NoSQL and NewSQL databases
• Cloud resource management
• Batch processing frameworks
• Stream processing frameworks
• Graph processing frameworks
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6. Course Material
Mainly based on research papers.
You will find all the material on the course web page:
http://www.sics.se/∼amir/cloud14.htm
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7. Course Examination
Mid term exam: 20%
Final exam: 20%
Reading assignments: 27%
Final presentation: 23%
Final project: 10%
Lab assignments: 0% :)
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8. Reading Assignments
Nine reading assignments.
You should write a review for each paper (at most two pages).
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9. Reading Assignments
Nine reading assignments.
You should write a review for each paper (at most two pages).
For each paper you should:
• Identify and motivate the problem.
• Pinpoint the main contributions.
• Identify positive/negative aspects of the solution/paper.
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10. Reading Assignments
Nine reading assignments.
You should write a review for each paper (at most two pages).
For each paper you should:
• Identify and motivate the problem.
• Pinpoint the main contributions.
• Identify positive/negative aspects of the solution/paper.
For each paper, you might be given some questions to answer.
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11. Reading Assignments
Nine reading assignments.
You should write a review for each paper (at most two pages).
For each paper you should:
• Identify and motivate the problem.
• Pinpoint the main contributions.
• Identify positive/negative aspects of the solution/paper.
For each paper, you might be given some questions to answer.
Students will work in groups of two.
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12. Presentation
Each group give a 30 minutes talk on a scientific paper.
The list of papers will be available in the course web page.
You are also free to choose any other paper, but it should be con-
firmed.
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13. Lab Assignments and the Project
Implement simple applications on different frameworks through the
course.
The solution of each lab assignment will be uploaded on the course
page, one week after their start dates.
The final project on top of Spark.
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14. Discussion Forum
Use the course discussion forum if you have any questions:
http://www.sics.se/∼amir/cloud14.htm
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15. Course Overview
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16. Data is not information, information is not knowledge, knowledge is
not understanding, understanding is not wisdom.
- Clifford Stoll
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17. Big Data
small data big data
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18. Big Data refers to datasets and flows large
enough that has outpaced our capability to
store, process, analyze, and understand.
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19. The Four Dimensions of Big Data
Volume: data size
Velocity: data generation rate
Variety: data heterogeneity
This 4th V is for Vacillation:
Veracity/Variability/Value
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20. Where Does
Big Data Come From?
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21. Big Data Market Driving Factors
The number of web pages indexed by Google, which were around
one million in 1998, have exceeded one trillion in 2008, and its
expansion is accelerated by appearance of the social networks.∗
[∗Wei Fan et al., Mining big data: current status, and forecast to the future, 2013]
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22. Big Data Market Driving Factors
The amount of mobile data traffic is expected to grow to 10.8
Exabyte per month by 2016.∗
[∗Dan Vesset et al., Worldwide Big Data Technology and Services 2012-2015 Forecast, 2013]
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23. Big Data Market Driving Factors
More than 65 billion devices were connected to the Internet by
2010, and this number will go up to 230 billion by 2020.∗
[∗John Mahoney et al., The Internet of Things Is Coming, 2013]
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24. Big Data Market Driving Factors
Open source communities
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25. Big Data Market Driving Factors
Many companies are moving towards using Cloud services to
access Big Data analytical tools.
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26. History of Data
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27. 4000 B.C
Manual recording
From tablets to papyrus, to parchment, and then to paper
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28. 1450
Gutenberg’s printing press
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29. 1800’s - 1940’s
Punched cards (no fault-tolerance)
Binary data
1890: US census
1911: IBM appeared
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30. 1940’s - 1950’s
Magnetic tapes
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31. 1950’s - 1960’s
Large-scale mainframe computers
Batch transaction processing
File-oriented record processing model (e.g., COBOL)
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32. 1960’s - 1970’s
Hierarchical DBMS (one-to-many)
Network DBMS (many-to-many)
VM OS by IBM → multiple VMs on a single physical node.
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33. 1970’s - 1980’s
Relational DBMS (tables) and SQL
ACID
Client-server computing
Parallel processing
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34. 1990’s - 2000’s
Virtualized Private Network connections (VPN)
The Internet...
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35. 2000’s - Now
Cloud computing
NoSQL: BASE instead of ACID
Big Data
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36. Cloud and Big Data
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37. Amir H. Payberah (Tehran Polytechnic) Introduction 1393/6/22 33 / 48

38. Big Data Analytics Stack
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39. Hadoop Big Data Analytics Stack
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40. Spark Big Data Analytics Stack
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41. Big Data - File systems
Traditional file-systems are not well-designed for large-scale data
processing systems.
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42. Big Data - File systems
Traditional file-systems are not well-designed for large-scale data
processing systems.
Efficiency has a higher priority than other features, e.g., directory
service.
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43. Big Data - File systems
Traditional file-systems are not well-designed for large-scale data
processing systems.
Efficiency has a higher priority than other features, e.g., directory
service.
Massive size of data tends to store it across multiple machines in a
distributed way.
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44. Big Data - File systems
Traditional file-systems are not well-designed for large-scale data
processing systems.
Efficiency has a higher priority than other features, e.g., directory
service.
Massive size of data tends to store it across multiple machines in a
distributed way.
HDFS/GFS, Amazon S3, ...
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45. Big Data - Database
Relational Databases Management Systems (RDMS) were not de-
signed to be distributed.
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46. Big Data - Database
Relational Databases Management Systems (RDMS) were not de-
signed to be distributed.
NoSQL databases relax one or more of the ACID properties: BASE
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47. Big Data - Database
Relational Databases Management Systems (RDMS) were not de-
signed to be distributed.
NoSQL databases relax one or more of the ACID properties: BASE
Different data models: key/value, column-family, graph, document.
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48. Big Data - Database
Relational Databases Management Systems (RDMS) were not de-
signed to be distributed.
NoSQL databases relax one or more of the ACID properties: BASE
Different data models: key/value, column-family, graph, document.
Hbase/BigTable, Dynamo, Scalaris, Cassandra, MongoDB, Volde-
mort, Riak, Neo4J, ...
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49. Big Data - Resource Management
Different frameworks require different computing resources.
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50. Big Data - Resource Management
Different frameworks require different computing resources.
Large organizations need the ability to share data and resources
between multiple frameworks.
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51. Big Data - Resource Management
Different frameworks require different computing resources.
Large organizations need the ability to share data and resources
between multiple frameworks.
Resource management share resources in a cluster between multiple
frameworks while providing resource isolation.
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52. Big Data - Resource Management
Different frameworks require different computing resources.
Large organizations need the ability to share data and resources
between multiple frameworks.
Resource management share resources in a cluster between multiple
frameworks while providing resource isolation.
Mesos, YARN, Quincy, ...
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53. Big Data - Execution Engine
Scalable and fault tolerance parallel data processing on clusters of
unreliable machines.
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54. Big Data - Execution Engine
Scalable and fault tolerance parallel data processing on clusters of
unreliable machines.
Data-parallel programming model for clusters of commodity ma-
chines.
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55. Big Data - Execution Engine
Scalable and fault tolerance parallel data processing on clusters of
unreliable machines.
Data-parallel programming model for clusters of commodity ma-
chines.
MapReduce, Spark, Stratosphere, Dryad, Hyracks, ...
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56. Big Data - Query/Scripting Language
Low-level programming of execution engines, e.g., MapReduce, is
not easy for end users.
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57. Big Data - Query/Scripting Language
Low-level programming of execution engines, e.g., MapReduce, is
not easy for end users.
Need high-level language to improve the query capabilities of exe-
cution engines.
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58. Big Data - Query/Scripting Language
Low-level programming of execution engines, e.g., MapReduce, is
not easy for end users.
Need high-level language to improve the query capabilities of exe-
cution engines.
It translates user-defined functions to low-level API of the execution
engines.
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59. Big Data - Query/Scripting Language
Low-level programming of execution engines, e.g., MapReduce, is
not easy for end users.
Need high-level language to improve the query capabilities of exe-
cution engines.
It translates user-defined functions to low-level API of the execution
engines.
Pig, Hive, Shark, Meteor, DryadLINQ, SCOPE, ...
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60. Big Data - Stream Processing
Providing users with fresh and low latency results.
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61. Big Data - Stream Processing
Providing users with fresh and low latency results.
Database Management Systems (DBMS) vs. Data Stream Man-
agement Systems (DSMS)
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62. Big Data - Stream Processing
Providing users with fresh and low latency results.
Database Management Systems (DBMS) vs. Data Stream Man-
agement Systems (DSMS)
Storm, S4, SEEP, D-Stream, Naiad, ...
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63. Big Data - Graph Processing
Many problems are expressed using graphs: sparse computational
dependencies, and multiple iterations to converge.
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64. Big Data - Graph Processing
Many problems are expressed using graphs: sparse computational
dependencies, and multiple iterations to converge.
Data-parallel frameworks, such as MapReduce, are not ideal for
these problems: slow
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65. Big Data - Graph Processing
Many problems are expressed using graphs: sparse computational
dependencies, and multiple iterations to converge.
Data-parallel frameworks, such as MapReduce, are not ideal for
these problems: slow
Graph processing frameworks are optimized for graph-based prob-
lems.
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66. Big Data - Graph Processing
Many problems are expressed using graphs: sparse computational
dependencies, and multiple iterations to converge.
Data-parallel frameworks, such as MapReduce, are not ideal for
these problems: slow
Graph processing frameworks are optimized for graph-based prob-
lems.
Pregel, Giraph, GraphX, GraphLab, PowerGraph, GraphChi, ...
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67. Big Data - Machine Learning
Implementing and consuming machine learning techniques at scale
are difficult tasks for developers and end users.
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68. Big Data - Machine Learning
Implementing and consuming machine learning techniques at scale
are difficult tasks for developers and end users.
There exist platforms that address it by providing scalable machine-
learning and data mining libraries.
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69. Big Data - Machine Learning
Implementing and consuming machine learning techniques at scale
are difficult tasks for developers and end users.
There exist platforms that address it by providing scalable machine-
learning and data mining libraries.
Mahout, MLBase, SystemML, Ricardo, Presto, ...
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70. Big Data - Configuration and Synchronization Service
A means to synchronize distributed applications accesses to shared
resources.
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71. Big Data - Configuration and Synchronization Service
A means to synchronize distributed applications accesses to shared
resources.
Allows distributed processes to coordinate with each other.
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72. Big Data - Configuration and Synchronization Service
A means to synchronize distributed applications accesses to shared
resources.
Allows distributed processes to coordinate with each other.
Zookeeper, Chubby, ...
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73. Summary
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74. Summary
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75. Questions?
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