Subject

Network Virtualisation and Cloud Computing

Course Title

Network Virtualisation and Cloud Computing
Network Virtualization and Cloud Computing

Code

m23_s_064

Study Programme

—

Organizer of the study programme (unit, institute, department or division)

Faculty of Computer Science and Engineering

Degree level (first, second, third cycle)

Second cycle

Academic year / semester

10 / Summer

Number of ECTS credits

Teacher

Igor Mishkovski

Prerequisites for enrolling in the course

—

10.

Objectives of the course programme (competences)

This course is designed to prepare students to understand the new technologies of network virtualization and cloud computing, their principles, modeling, analysis, design, and possible industry-oriented applications. Upon completion of this course, the student is prepared to build a career in application development and enabling services that run on the distributed network by using virtual resources.

11.

Course content

Virtualization concepts, components, and infrastructure. Infrastructure-level virtualization. Hardware and software virtualization. CPU virtualization. Storage virtualization. SAN, iSCSI. Network virtualization. VLAN. Management of
The life cycle of virtual machines. Virtualization services. Concepts of cloud computing, evolution, architectures, infrastructures, capabilities, risk, company adaptation strategies, standards and policies, Software-as-a-Service (SaaS), Platform-as-a-Service (PaaS), Infrastructure-as-a-Service (IaaS), modern cloud computing technologies and tools. Cloud computing security. Real-world scenarios and team project development. Azure platform: introduction to cloud services, overview of the Azure platform, Azure storage, Azure Application Factory, SQL Azure. Amazon EC2, Amazon S3, Amazon DB, Redshift and CloudFront. Large data sets and their management. MapReduce. Integration of IoT solutions in the cloud

12.

Learning methods

Lectures supported by slide presentations, interactive lectures, practical classes (using equipment and software packages), teamwork, case studies, guest lecturers, independent preparation and defence of a project assignment and seminar paper, and learning in an electronic environment (forums and consultations).

13.

Total available time

6 ECTS x 30 hours = 180 hours

14.

Distribution of available time

45 + 15 + 30 + 50 + 40 = 180 hours

15.

Forms of teaching activities

15.1.	Lectures - theoretical instruction	45 hours
15.2.	Exercises (laboratory, auditory), seminars, teamwork	15 hours

16.

Other forms of activities

16.1.	Project assignments	50 hours
16.2.	Independent assignments	30 hours
16.3.	Home study	40 hours

17.

Assessment method

17.1.	Tests	45 points
17.2.	Seminar paper / project (presentation: written and oral)	50 points
17.3.	Activities and learning	10 points
17.4.	Final exam	0 points

18.

Grading criteria (points / grade)

up to 50 points	5 (five) (F)
from 51 to 60 points	6 (six) (E)
from 61 to 70 points	7 (seven) (D)
from 71 to 80 points	8 (eight) (C)
from 81 to 90 points	9 (nine) (B)
from 91 to 100 points	10 (ten) (A)

19.

Requirement for obtaining a signature and taking the final exam

Activities 15 and 16 implemented

20.

Language of instruction

Macedonian and English

21.

Method for monitoring the quality of teaching

internal evaluation and survey mechanism

22.

Literature

22.1.	Required literature 1. Dac-Nhuong Le, Raghvendra Kumar, Gia Nhu Nguyen, Jyotir Moy Chatterjee \| Cloud Computing and Virtualization \| Wiley-Scrivener; 1st edition (April 3, 2018) \| 2018 2. Kai Hwang \| Cloud Computing for Machine Learning and Cognitive Applications \| The MIT Press \| 2017 3. Monika Mangla \| Integration of Cloud Computing with Internet of Things \| Wiley-Scrivener \| 2021
22.2.	Additional literature No. Author Title Publisher Year