DAT351 Distributed Computing Resources for High Volume Data Processing

Course description for academic year 2017/2018

Contents and structure

This course presents technology and principles of grid and cloud computing, and gives a practical introduction to grid middleware. The course also covers topics from current research in development and use of grid technologies, including the use of cloud resources for grid computing.

Learning Outcome

Upon completion of the course the student will be able to

  • explain the philosophy of grid and cloud computing
  • discuss status and limitation of current grid operation
  • identify tasks well suited for execution on grid systems
  • assess selected research papers in the field of grid and cloud computing
  • explain the different cloud service models
  • describe the different hypervisor models used for virtualization

  • install and configure a selected grid middleware system (currently Globus)
  • define and monitor job management, storage management and security in a grid system
  • design applications of Service Oriented Computing at a global scale
  • adapt software to benefit from cloud resources

  • evaluate and use grid computing resources using textual and graphical interfaces
  • revise application software to make it suitable for grid execution

Entry requirements

General admission requirements for the study programme. Experience with using a Unix/Linux operating system is an advantage.

Teaching methods

Lectures, practical work in lab, presentation of papers and project work.

Compulsory learning activities

2-4 assignments in the form of written reports.

The assignments must be submitted within set deadlines and must be approved before examination can take place.

Approved assignments are valid for the examination semester and 2 following semesters.

Assessment

The course has an examination in two parts: an oral exam and a project report. The project report counts for 30% of the final grade and the oral exam counts for 70% of the final grade.

Both parts must get a passing grade in order to get a final grade for the course. In case one of the parts gets a failing grade, that part can be taken as a re-sitting/postponed exam.

Grading scale is A-F where F is fail.

Course reductions

  • PCS951 - Distribuerte datasystem for høgvolum databehandling - Reduction: 10 studypoints