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Module Specifications

Current Academic Year 2012 - 2013
Please note that this information is subject to change.

Module Title Cloud Technologies
Module Code CA675
School School of Computing
Online Module Resources

Module Co-ordinatorSemester 1: Grace Ramamoorthy
Semester 2: Grace Ramamoorthy
Autumn: Grace Ramamoorthy
Module TeacherGrace Ramamoorthy
NFQ level 8 Credit Rating 7.5
Pre-requisite None
Co-requisite None
Compatibles None
Incompatibles None
Description
Big Data Cloud Computing (BDCC) focuses on efficiently executing large-scale computations over massive data sets. BDCC requires the storage, organization, and processing of data at a scale and efficiency that go well beyond the capabilities of conventional information technologies. As such, the industry has taken the lead on building data centers and BDCC systems to successfully provide online web services and analyze data for internal business goals at unprecedented scale, efficiency and availability. The BDCC model has the potential to radically change the way we capitalize on data and produce breakthroughs in other areas including science, engineering, health care and security.

Learning Outcomes
1. * Implement massively parallel data processing using high-level programming primitives
2. *Read and analyse technical papers and research publications
3. *Write programmes using Google's Map-Reduce, Apache Hadoop, Amazon Web Services, and NVIDIA's CUDA
4. *Design prototype data-intensive applications using existing Big Data Cloud Computing tools and platforms



Workload Full-time hours per semester
Type Hours Description
Lecture24Full class notes are provided in booklet form in advance of lectures and material is divided by lecture and topics covered. Course content, including supplementary material on key topics and a short interactive tutorial are available online
Laboratory70Project related work in labs.
Independent learning time94This comprises time for reading, reviewing given and other exercises, group interaction on project, project time and write-up and revision
Total Workload: 188

All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml

Indicative Content and Learning Activities
Big Data Cloud Computing (BDCC).
Big Data Cloud Computing (BDCC)focuses on efficiently executing large-scale computations over massive data sets. BDCC requires the storage, organization, and processing of data at a scale and efficiency that go well beyond the capabilities of conventional information technologies. As such, the industry has taken the lead on building data centers and BDCC systems to successfully provide online web services and analyze data for internal business goals at unprecedented scale, efficiency and availability. The BDCC model has the potential to radically change the way we capitalize on data and produce breakthroughs in other areas including science, engineering, health care and security.This course will investigate the state of the art in BDCC systems. We will study the existing BDCC platforms, models, and tools as well as the open research challenges, with an emphasis on massively parallel data processing using high-level programming primitives. The topics will include Google's Map-Reduce, Apache Hadoop, Amazon Web Services, and NVIDIA's CUDA. The course will primarily consist of technical readings and discussions. It will also include programming projects where the participants will implement prototype data-intensive applications using existing BDCC tools and platforms..

Assessment Breakdown
Continuous Assessment0% Examination Weight100%
Course Work Breakdown
TypeDescription% of totalAssessment Date
ProjectThe students complete projects, which will involve prototyping applications using existing Cloud Computing tools and platforms for Big Data30%Week 7
ProjectThe students complete projects, which will involve prototyping applications using existing Cloud Computing tools and platforms for Big Data30%Week 11
Reassessment Requirement
Resit arrangements are explained by the following categories;
1 = A resit is available for all components of the module
2 = No resit is available for 100% continuous assessment module
3 = No resit is available for the continuous assessment component
This module is category 1
Indicative Reading List
  • Borko Furht, Armando Escalante: 2010, Handbook of Cloud Computing, Springer, 1441965238, 9781441965233
Other Resources
3673, WEBSITE, 0, http://www.computing.dcu.ie/~ray/amazoncloud.pdf, 3674, WEBSITE, 0, http://www.computing.dcu.ie/~ray/cuda.pdf, 3675, WEBSITE, 0, http://www.computing.dcu.ie/~ray/mapreduce.pdf,
Array
Programme or List of Programmes
ECSAStudy Abroad (Engineering & Computing)
MCMM.Sc. in Computing
Timetable this semester: Timetable for CA675
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