Course: Cloud Computing and Storage (UF)

Project Information

Discipline
Computer Science (401) 
Subdiscipline
14.09 Computer Engineering 
Orientation
Education 
Abstract

EEL 6935: Cloud Computing and Storage (Fall 2013)

Course Objective and Description:

Using large-scale computing systems to solve data-intensive realworld problems has become indispensable for many scientific and engineering disciplines. This course provides a broad introduction to the fundamentals in cloud computing and storage, focusing on system architecture, programming models, algorithmic design, and application development. Selected scientific applications will be used as case studies. 

Prerequisite: introduction to programming or data structures and algorithms (EEL4834 or equivalent), computer architecture (EEL5764 or equivalent), proficiency in Java, or instructor approval. 

Textbook: 

  • Hadoop: The Definitive Guide (3rd Edition), Tom White, O'Reilly Media, 2012.

Other References: 

  • Many recent papers in leading conferences/journals will be discussed.
  • Data-Intensive Text Processing with MapReduce, Jimmy Lin and Chris Dyer, 2010. (PDF version available online)
  • Engineering Software as a Service: An Agile Approach Using Cloud Computing, Armando Fox and David Patterson, Strawberry Canyon, 2013.
  • The Way To Go: A Thorough Introduction To The Go Programming Language, Ivo Balbaert, iUniverse, 2012.
  • Programming Amazon EC2, Jurg van Vliet and Flavia Paganelli, O'Reilly Media, 2011.
  • Programming Google App Engine, Dan Sanderson, O'Reilly, 2012.
  • Distributed and Cloud Computing: From Parallel Processing to the Internet of Things by Kai Hwang, Jack Dongarra & Geoffrey C. Fox, Morgan Kaufmann, 2011.
  • The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines, Luiz Andre Barros and Urs Hoelzle, Morgan and Claypool Publishers, 2009.
  • The Grid: Blueprint for a New Computing Infrastructure (2nd Edition), Ian Foster, Carl Kesselman, Morgan Kaufmann/Elsevier, 2004.
  • The Fourth Paradigm: Data-Intensive Scientific Discovery, Tony Hey, Stewart Tansley, and Kristine Tolle, Microsoft Research, 2009. (PDF version available online)

Course Homepage:

    http://www.andyli.ece.ufl.edu/teaching/cloud

Course Outline (tentative):

  1. Introduction and Overview
  2. Programming Paradigms
  3. Introduction to Hadoop 
  4. MapReduce Runtime Management
  5. Algorithm Design and Implementation in MapReduce 
  6. Consistency and Coordination
  7. Key-Value Structured Storage
  8. Enhancements to Hadoop/MapReduce
  9. Distributed File Systems
  10. Case Study

Intellectual Merit

Covers basics in cloud computing and storage, touches the evolving cloud services and big data, integrates models, systems, programming, and performance evaluation.

Broader Impacts

Benefits about 80 graduate students.

Project Contact

Project Lead
Andy Li (andyli) 
Project Manager
Min Li (minli) 
Project Members
Min Li, Yang Ou, Venkata Vadrevu, Wenjie Sun, Satish Menedi, Vishwanath Vuradi, Radha Krishna Murthy Kandula, Kumar Sadhu, Pankaj Narula, Mohan Das Katragadda, Srikanth Thiruvadandam Porethi, Akhil Karanth, Yahui Han, Shravan Pentamsetty, Nakul Jindal, Prabal Kanodia, Priyanka -, sivasrinivas amara, Tirumala Naidu Tumati, Yu Sheng, Aniket Oak, Pratik Somanagoudar, Risheng Wang, Pratik Jain, Prajwal Narasimhamurthy, Saurav Majumder, Vivitsu Maharaja, Gaurab Dey, Marc-Anthony Tucker, Eunju Kim, Gopikrishnan Narayanan Kutty Sreelatha, Abhijeet Nayak, Dhananjay Bhirud, Divya Ginjupalli, Kyuho Jeong, Vijay Bhaskar Reddy Maddireddy, Aneesh Male, Kushal Arora, Devesh Gade, Xiao Shan, Maxine Hu, WEI LIN, Ruijia Xi, Aadhavan Ramesh, Srivattsan Sridharan, Balaji Iyer, Shrutikirti Patkar, Hrishikesh Jayathirtha, Rohan Indurkar, Shweta Shridhar, Garima Tiwari, Nachiket Patil, Sreekanth Kolla, Gengtao Jia, Karthik Ravi, Xu Wei, Kairan Sun, Abhijit Verma, Nitin Kosuri, Jaideep Bethu, Srikanth Kota, Vishwanath Patil, Akshatha Somayaji, Ruizhi Li, Haotian Sun, Yi Su, Xiaorui Wu, Ravindra Dangar, Harsha Galla, Sindhu Suryanarayana, Mohanasundaram Veeramuthu, Dhruva Bharadwaj  

Resource Requirements

Hardware Systems
  • alamo (Dell optiplex at TACC)
  • foxtrot (IBM iDataPlex at UF)
  • hotel (IBM iDataPlex at U Chicago)
  • india (IBM iDataPlex at IU)
  • sierra (IBM iDataPlex at SDSC)
  • delta (GPU Cloud)
 
Use of FutureGrid

We plan to use FutureGrid for students course projects: (1) programming, mainly Hadoop/MapReduce; (2) systems, mainly openstack (3) potentially GPU.

Scale of Use

We have near 80 students, roughly in 16 teams. Pretty light programming assignments will be on FutureGrid, and compare performance on FutureGrid with AWS EC2.

Project Timeline

Submitted
08/22/2013 - 10:37 
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