Checking date: 09/07/2020


Course: 2020/2021

Internet network architecture
(18449)
Study: Bachelor in Telematics Engineering (215)


Coordinating teacher: GARCIA MARTINEZ, ALBERTO

Department assigned to the subject: Department of Telematic Engineering

Type: Electives
ECTS Credits: 3.0 ECTS

Course:
Semester:




Competences and skills that will be acquired and learning results. Further information on this link
The 'Internet Architecture' course is devoted to understand how Internet works at a global scope in the current moment. We will understand how networks are connected one to each other, which are the economic incentives and their implications in the way networks connect along with the roles played by the different agents involved. One of the most relevant problems Internet faces is IP address scarcity (in the Internet Protocol version most widely used, IPv4). We will understand the magnitude of the problem. We will also understand the current deployed solutions: different ways of using private addressing (NAT, VPNs, etc.), IPv4 address market, and the development of a new protocol with a larger address space, IPv6. We will discuss the implications of each of these solutions to network functionality. Regarding to the methodology for the course, the approach followed is practical, focused on the analysis of real data and the deployment (in a virtual environment) of the proposed solutions. We intend knowledge to emerge from the access, processing and analysis of real data, and from the experience in the configuration of network scenarios. The objective is to empower the student to access by himself to the data/experience and build from this input its own knowledge.
Description of contents: programme
1. Introduction to Python. 1.1 Data processing with Python: 'pandas' library 2. Internet Architecture. 2.1 Interdomain routing. BGP routing. Pricing and relationships between networks. Internet business model 2.1.1 Quantitative analysis of the current Internet. 2.2 Content Data Networks (CDN), cloud, datacenters 2.2.1 Quantitative análisis of connectivity to CDNs 3. Addressing in the Internet 3.1 Public address assignment governance and policies. Address scarcity. Address market. 3.1.1 Quantitative analysis of assigned addresses. 3.1.2 IP address market 3.2 Use of private addresing 3.2.1 Tunnels 3.2.2 NATs and Carrier Grade NATs (CGNs) 3.2.1.1 Configuring NATs 3.2.2 Virtual Private Networks 3.2.2.1 Configuring VPNs 3.3. IPv6 addressing 3.3.1 Configuring IPv6 addresses
Learning activities and methodology
Regarding to the methodology for the course, the approach followed is practical, focused on the analysis of real data and the deployment (in a virtual environment) of the proposed solutions. We intend knowledge to emerge from the access, processing and analysis of real data, and from the experience in the configuration of network scenarios. The objective is to empower the student to access by himself to the data/experience and build from this input its own knowledge. Data analysis is a skill growingly required in the job market, that is just assumed to be known by any engineer. In this course we provide basic knowledge to data processing through a programming interface. For real data analysis, we use Python, and in particular the pandas library, a tool providing flexible data processing with a low entry barrier (we devote some course time to present these tools). We apply the tool to real data to analyse in the laboratory how many different networks are in the Internet, which is the distance between them, how many addresses have been assigned to date, who is the owner, which are the top buyers and sellers, how many routers are traversed when accessing to most popular destinations, etc. On the other hand, we use virtual network topologies (using the CORE virtual network framework) to understand how NATs are configured.
Assessment System
  • % end-of-term-examination 20
  • % of continuous assessment (assigments, laboratory, practicals...) 80
Basic Bibliography
  • Iljitsch van Beijnum. BGP. O'Reilly.
  • Sam Lau, Joey Gonzalez, and Deb Nolan.. Principles and Techniques of Data Science.. https://www.textbook.ds100.org/. 2019
Additional Bibliography
  • Ivan Vidal, I. Soto. Multimedia Networking Technologies, Protocols, & Architectures. Artech House Communications and Network Engineering. 2019
  • Wes McKinney. Python for data analysis. . O'Reilly Media, Inc.. 2017
Recursos electrónicosElectronic Resources *
Detailed subject contents or complementary information about assessment system of B.T.
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The course syllabus and the academic weekly planning may change due academic events or other reasons.