PENG9550 Cloud Computing and Security Emneplan

Cloud computing is an emerging paradigm of utilising large-scale computing services over the internet that will affect the computing needs of individuals and organisations. Over the past decade, many cloud computing platforms have been set up by companies such as Google, Yahoo!, Amazon, Microsoft, Salesforce, eBay and Facebook. Some of the platforms are open to the public via various pricing models. They operate at different levels and enable businesses to harness different amounts of computing power from the cloud.

The course will be offered once a year, provided 3 or more students sign up for the course. If less than 3 students sign up for a course, the course will be cancelled for that year.

Individual written exam, 3 hours.

The exam result can be appealed.

Students who complete the course are expected to have the following learning outcomes, defined in terms of knowledge, skills and general competence:

Knowledge

On successful completion of the course, the student:

• has a deep understanding of how cloud computing, large services and infrastructures play a crucial role in todays digitised society;
• has an interdisciplinary view on cloud computing due to its central role in the digitised society;
• understands the fundamental principles of distributed computing and particularly cloud computing;
• understands the importance of virtualisation in distributed computing and how this has enabled the development of cloud computing;
• understands the business models that underlie cloud computing;
• has an understanding of the architecture and concept of different cloud models: IaaS, PaaS, and SaaS;
• is knowledgeable in the various methods available to monitor and evaluate cloud infrastructure;
• has a deep knowledge of the common security issues in the field of cloud computing;
• has an understanding of the concept of threat intelligence in the field of cloud computing;
• understands the use of security policies as part of the overall security strategy of an organization;

Skills

On successful completion of the course, the student can:

• design highly distributed digital systems.
• create virtual machine images and deploy them on a cloud.
• design and develop scalable cloud-based applications by creating and configuring virtual machines in the cloud.
• analyse cloud infrastructures with regard to properties such as resilience, security, performance and manageability.
• identify cloud security weaknesses by recognising and discovering threats and vulnerabilities to cloud computing.
• implement cloud features to secure and harden the infrastructure.
• use tools to monitor and evaluate cloud infrastructure.
• use tools to analyse system logs to detect possible security or performance problems.

General competence

On successful completion of the course, the student:

• can discuss his/her area of expertise with a non-expert audience by combining insights across disciplines.
• can discuss and debate the impact of technological development on our society in the future

All printed and written aids.

Calculator.

The course is part of a series of seminars in which the students actively participate together with members of relevant research groups. The students present papers and listen to paper presentations from other PhD students and staff. The students are also expected to actively critique and challenge fellow participants. The students are provided with a sound foundation in research skills and are naturally integrated into the local research community and its research discourse.

This course, together with Mathematics 1000, will give the students an understanding of mathematical concepts, problems and solution methods with the focus on application, particularly in engineering subjects.

There are no requirements beyond the admission requirements.

After completing the course, the student is expected to have achieved the following learning outcomes defined in terms of knowledge, skills and general competence:

Knowledge

The student is capable of:

• explaining how functions can be approximated by Taylor polynomials and truncated Fourier series
• explain what it means that a series converge, with emphasis on Taylor and Fourier series
• differentiate and integrate power series term by term
• explaining what a frequency spectrum is and explaining the principle of filtering signals in the frequency domain
• describing and explaining how a sequence of numbers can originate by sampling, by using formulae or as the solution of a difference equation
• explaining how to interpolate sampled data
• explaining partial differentiation and using different graphical ways to describe and visualize functions of two variables
• calculating eigenvalues and eigenvectors of matrices and giving geometrical interpretations of these values

Skills

The student is capable of:

• discussing the connection between Fourier series and the Fourier transform
• discussing pros and cons using interpolating polynomials, and splines to interpolate sampled data
• explaining how the method of least square may be applied to fit data to a linear function
• discussing error bounds when using Taylor polynomials to approximate functions
• using simple tests of convergence of series, for example the ratio test
• giving a geometrical interpretation of gradient and directional derivative and using linear approximation and total differential of functions of two variables to calculate uncertainty
• using partial differentiation optimize functions of two variables
• using eigenvalues and eigenvectors to solve systems of differential equations with constant coefficients

General competence

The student is capable of:

• identifying the connection between mathematics and their own field of engineering
• translating a practical problem from their own field into mathematical form, so that it can be solved analytically or numerically
• using mathematical methods and tools that are relevant to their field of engineering
• assessing the results of mathematical calculations and using basic numerical algorithms

The course is taught through joint lectures and exercises. In the exercise sessions, the students work on assignments, both individually and in groups, under the supervision of a lecturer. These sessions will also involve assessing the assignments - both own ones and assignments carried out by fellow students.

In between teaching sessions, the students are supposed to work with exercises. The proposed exercises are directly linked to learning outcomes for the course. Assessing their own and others' solutions will provide the students with insight as to which extent these goals are achieved.

The students will also have the option of handing in certain exercise sets and have these assessed.

None.