COM 444 Cloud Computing




      Lecture Schedule (tentative and read Important Notes below)




 Topics Covered





Lec 1

Distributed System Models and Enabling Technologies
Scalable Computing over the Internet, Technologies for Network-Based Systems, System Models for Distributed and Cloud Computing, Software Environments for Distributed Systems and Clouds, Performance, Security
2 7/10   National Holiday - Kurban Bayramı




Lec 2

Computer Clusters for Scalable Computing
Clustering for Massive Parallelism, Computer Clusters and MPP Architectures, Design Principles of Computer Clusters, Cluster Job and Resource Management, Case Studies of Top Supercomputer Systems

- What is Parallel Computing?


4 21/10 Lec 3 Virtual Machines and Virtualization of Clusters and Datacenters
Implementation Levels of Virtualization, Virtualization Structures/Tools and Mechanisms, Virtualization of CPU, Memory, and I/O Devices, Virtual Clusters and Resource Management, Virtualization for Data-Center Automation


Lec 4

Cloud Platform Architecture over Virtualized Data Centers:
Data Center Design and Networking
What is a Data Center? What does a Data Center Look Like? Warehouse-Scale Data Center Design, Power and Cooling Requirements, Data-Center Interconnection Networks, Design Considerations for WSC

Videos on Data Centers:

- Explore a Google Data Center with Street View

- Google Container Data Center




Lec 5

Cloud Platform Architecture over Virtualized Data Centers:
Cloud Computing Service Models
Cloud Computing Services Stack, Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS), Today’s Cloud Services Stack, Public, Private & Hybrid Clouds, Market-Oriented Cloud Architecture, Inter-Cloud Resource Management, Cloud Security and Trust Management
7 11/11 Lec 6 Cloud Platform Architecture over Virtualized Data Centers:
Major Cloud Service Providers
Public Clouds, Amazon Web Services (AWS), Google App Engine, Microsoft Azure

- Amazon Web Services (AWS)    Getting Started with AWS
- Introduction to Amazon Web Services (video tutorial)

- Good App Engine

- Introduction to Google App Engine For Developers (video tutorial)

- Microsoft Azure

8 18/11 Midterm Exam


Lec 7

Lec 7.1

Lec 8


Service Oriented Architectures: Fundamentals
Introduction, Web Services, Service Descriptions and IDL for Web Services, A Directory Service for Use with Web Services, XML Security, Coordination of Web Services, Applications of Web Services, REST Style Web Services

Service Oriented Architectures
Services and Service-Oriented Architecture, Message-Oriented Middleware, Portals and Science Gateways, Discovery, Registries, Metadata, and Databases, Workflow in Service-Oriented Architectures

10 2/12   The Professor attends Bulut Bilşim ve Büyük Veri Çalıştayı 2014 in Istanbul    
11 9/12 Lec 9

Lec 9.1

Lec 9.2

Cloud Programming and Software Environments (1/2)
What is Big Data? New Parallel Programming Paradigm: MapReduce, The MapReduce Programming Model, Hadoop, Hadoop 1.0 vs 2.0, Writing Jobs for Hadoop, Hadoop Distributed File System (HDFS), Hadoop Internals, MapReduce Cloud Service

- The Google File System, S. Ghemawat et al., SOSP, 2003.

- MapReduce: Simplied Data Processing on Large Clusters, J. Dean, S. Ghemawat, OSDI, 2004.

- Hadoop home page

- Beyond Batch- The Evolution of the Hadoop Ecosystem - Doug Cutting

- HDFS-Comics   

12 16/12   The students attends Amazon Web Services Workshop Days in Izmir
Midterm Exam II - Take Home Exam

- MapReduce Tutorial (Apache Hadoop 1.2.1) 
- MapReduce Tutorial (Apache Hadoop 2.6.0)
- Google MapReduce Tutorial

13 23/12 Lec 10 Cloud Programming and Software Environments (2/2)
Features of Cloud and Grid Platforms, Parallel and Distributed Programming Paradigms, Programming Support of Google App Engine, Programming on Amazon AWS and Microsoft Azure, Emerging Cloud Software Environments


- Hadoop Tutorial: Introducing Apache Hadoop (17 minutes) 
- Hadoop Tutorial: Intro To Hadoop Developer Training | Cloudera (1 hour)
- Hadoop - Just the Basics for Big Data Rookies (1 hour 25 minutes) 
- Big Data and Hadoop Tutorials - 28 Videos and 20 hours -
- Hadoop MapReduce Fundamentals 1 of 5 
- Intro To MapReduce 

14 30/12   Project Demonstrations    

  Important Notes


This is the syllabus (Course Information Form) given to students at the beginning of the semester.


The lecture schedules given in the syllabus are tentative and updated here weekly. Look at this table once a week.


Almost all the slides used during the semester will be available here.


You can download the previous year’s lecture slides before the class from this address.


You can download the new lecture slides presented in the class after the lecture from this page.


I may skip several slides during the lecture (The slides given would be generally too much!). They are included in the course material for completeness and to provide a good reference for your future professional engineering life.


To follow the lecture and understand the materials presented in class better, get the lecture slides and take the print-outs of them, and please bring them to class.


Purposes for bringing slides to class: 1) To allow better concentration in lecture by reducing note-taking pressure and to provide a study-aid before and after lecture.


2) You can take your notes on these slides and be active during the lecture. You digest material much better when you actively take notes from step-to-step demonstrations given by your instructor than by just sitting and watching slides.


Disclaimers: (a) I may not follow these slides exactly in class (b) I may also use the whiteboard and give some extra notes which will not be posted here as needed in class (c) Students are responsible for what I say and teach in class. (d) Reading these slides is not a substitute for attending lecture.

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