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Course syllabus - Smart Digital Platforms: Cloud Computing, Security and Big Data

Scope

7.5 credits

Course code

DVA260

Valid from

Autumn semester 2022

Education level

First cycle

Progressive Specialisation

G1F (First cycle, has less than 60 credits in first-cycle course/s as entry requirements).

Main area(s)

Computer Science

School

School of Innovation, Design and Engineering

Ratified

2020-12-15

Revised

2022-01-24

Literature lists

Course literature is preliminary up to 8 weeks before course start. Course literature can be valid over several semesters.

    • Ratified date: 2022-11-17
    • Latest update: 2022-11-17

    Books

    Ruparelia, Nayan

    Cloud computing

    Cambridge, Massachusetts : The MIT Press, [2016] - xiii, 260 pages

    ISBN: 9780262529099 LIBRIS-ID: 19806677

    Silberschatz, Abraham; Galvin, Peter Baer.; Gagne, Greg.

    Operating system concepts

    9. ed. : Hoboken, N.J. : Wiley, 2013 - xxii, 944 p.

    ISBN: 9781118063330 LIBRIS-ID: 13898924

    Marz, Nathan; Warren, James

    Big data : principles and best practices of scalable real-time data systems

    Shelter Island, NY : Manning, cop. 2015 - xx, 308 p.

    ISBN: 9781617290343 LIBRIS-ID: 18715698

    Pfleeger, Charles P.; Pfleeger, Shari Lawrence; Margulies, Jonathan

    Security in computing

    Fifth edition : Upper Saddle River, NJ : Prentice Hall, [2015] - xxxiii, 910 pages

    ISBN: 9780134085043 LIBRIS-ID: 18407818

    Not mandatory.

    Bishop, Matthew

    Computer security : art and science

    Boston : Addison-Wesley, cop. 2003 - xli, 1084 s.

    ISBN: 0201440997 LIBRIS-ID: 8621524

    Not mandatory.

    Articles

    Mell, P.; Tim, G.

    The NIST definition of cloud computing

    NIST, 2011

    Liu, F.; Tong, J.; Mao, J.; Bohn, R.; Messina, J.; Badger, L.; Leaf, D.

    NIST cloud computing reference architecture

    NIST, 2011

    NIST special publication. 500(2011):1-28

    Al-Dulaimy, A.; Sharma, Y.; Khan, M.G.; Taheri, J.

    Introduction to edge computing

    2020

    Edge Computing: Models, technologies, and applications. 2020:3-25.

    Not mandatory.

    Web adresses

    URL: CISCO

    URL: IBM

    Not mandatory.

    URL: Amazon

    URL: Amazon

    Not mandatory.

    URL: Google

    Not mandatory.

    URL: GeeksforGeeks

    Not mandatory.

Objectives

The purpose of this course is to introduce the concept of cloud computing and give knowledge and insights in handling and processing Big Data in the cloud. The course will provide students with the necessary theoretical and practical knowledge to be able to use cloud services in managing Big Data and taking into account the data security aspects.

Learning outcomes

After completing the course, the student shall be able to:
 
1. describe the cloud models, service models, structure and functionalities,
2. describe the concept of Big Data and relation to cloud services,
3. differentiate between different database services and apply them in different scenarios,
4. understand and compare different storage options,
5. describe and recommend how data is secured in cloud environment and
6. practically apply a number of cloud services to search large amounts of data.

Course content

- Introduction to Cloud Computing: Concept, Architecture, Services, and Deployment models
- Cloud technologies: Virtualization, Load Balancing, Scalability and Elasticity
- Cloud storage: Overview of storage technologies
- Cloud databases: Database concept and languages
- Big Data in the cloud: Concept and analytic services
- The de facto Big Data processing frameworks: Hadoop and MapReduce
- Security and Privacy in the cloud: Concept and challenges
- Cloud Access: authentication, authorization and accounting

Specific requirements

Completed the courses Programming 7,5 credits and Information Security 7,5 credits.

Examination

Written examination (TEN1), 2,5 hp, examines the learning outcomes 1-5, marks Fail (U), 3, 4 or 5.
Project  (PRO1), 3 hp, examines the learning outcome 6, marks Fail (U), 3, 4 or 5.
Laboratory work (LAB1), 2 hp, examines the learning outcomes 2-4, marks Fail (U) or Pass (G).

A student who has a certificate from MDU regarding a disability has the opportunity to submit a request for supportive measures during written examinations or other forms of examination, in accordance with the Rules and Regulations for Examinations at First-cycle and Second-cycle Level at Mälardalen University (2020/1655). It is the examiner who takes decisions on any supportive measures, based on what kind of certificate is issued, and in that case which measures are to be applied.

Suspicions of attempting to deceive in examinations (cheating) are reported to the Vice-Chancellor, in accordance with the Higher Education Ordinance, and are examined by the University’s Disciplinary Board. If the Disciplinary Board considers the student to be guilty of a disciplinary offence, the Board will take a decision on disciplinary action, which will be a warning or suspension.

Grade

Pass with distinction, Pass with credit, Pass, Fail

Interim Regulations and Other Regulations

The course overlaps with 3 credits towards DVA453 Machine Learning With Big Data, with 4,5 credits towards DVA254 Cloud Services and with 2,5 credits towards DVA 444 Industrial Systems in Cloud Computing.