CSCE 5013 Cloud Computing (Spring 2018)

 

Course Description: Cloud computing has entered the mainstream of information technology, providing infinite or at least highly elastic scalability in delivery of enterprise applications and software as a service (SaaS). Amazon Elastic Compute Cloud (EC2), Microsoft Azure, Google Cloud Platform, and a few other offerings give both mature software vendors and start-ups the option to deploy their applications to a system of infinite computational power with practically no capital investment and with modest operating costs proportional to the actual use. In this course, we will focus on the architecture of today’s cloud computing client systems, the evolution of the Internet to support the cloud, the architecture of modern cloud data centers, the technologies used within them, and how to develop applications in the cloud using MapReduce and Spark.
   
Credit hours: 3
   
Meetings:

Lecture: M/W/F 11:50 AM - 12:40 PM, JBHT 239

   
Instructor:

Miaoqing Huang

Office: JBHT 526

Phone: 479-575-7578

Email: mqhuang AT uark.edu

   
Office Hours:

Monday 10:30 AM - 11:30 AM, Wednesday 1:00 PM - 2:00 PM

   
Textbook:

None. Handout and other course materials will be given in the class.

   
Syllabus: Download here.

 

 

 

Class Schedule: (subject to change)

 

 

Week

Date

Content

Lecture

Note

1

1/17 Course introduction and syllabus Lecture_1  
1/19 Basic Concepts of Cloud Computing Lecture_2  
2 1/22 Enabling Technologies Lecture_3 Handout: supercomputers vs datacenters
1/24 Cloud Computing Architectures Lecture_4  
1/26 Introduction to MapReduce Lecture 5  
3 1/29      
1/31 MapReduce algorithm design Lecture 6  
2/2      
4 2/5      
2/7      
2/9
5 2/12      
2/14      
2/16      
6 2/19      
2/21 Inverted Indexing Lecture 7  
2/23      
7 2/26      
2/28 Graph algorithms Lecture 8  
3/2      
8 3/5      
3/7      
3/9      
9 3/12      
3/14      
3/16 Introduction to Spark Lecture 9  
10 3/19     Spring break
3/21     Spring break
3/23     Spring break
11 3/26      
3/28  
3/30      
12 4/2      
4/4      
4/6      
13 4/9      
4/11      
4/13 Working with Key/Value Pairs Lecture 10  
14 4/16
4/18      
4/20      
15 4/23      
4/25      
4/27      
16 4/30      
5/2      
5/4     Dead day
17 5/9 Final exam   12:45PM - 2:45PM

 

    

Exam:

 

 

 

Grading:

 

    A: over 90%

    B: 80% - 89%

    C: 70% - 79%

    D: 60% - 69%

    F: below 60%

  

    Course tasks are weighed using the following scale:

 

    Midterm Exam:                           20%

    Final Exam:                               25% 

    Programming Assignments:           50% 

    Quiz and Attendance:                 5%