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Network measurement laboratory

01QWPBG, 01QWPOV

A.A. 2022/23

Course Language

Inglese

Course degree

Master of science-level of the Bologna process in Communications And Computer Networks Engineering (Ingegneria Telematica E Delle Comunicazioni) - Torino
Master of science-level of the Bologna process in Ingegneria Informatica (Computer Engineering) - Torino

Course structure
Teaching Hours
Lezioni 20
Esercitazioni in laboratorio 40
Teachers
Teacher Status SSD h.Les h.Ex h.Lab h.Tut Years teaching
Mellia Marco Professore Ordinario ING-INF/03 10 0 30 0 7
Teaching assistant
Espandi

Context
SSD CFU Activities Area context
ING-INF/03 6 B - Caratterizzanti Ingegneria delle telecomunicazioni
2022/23
This is a laboratory and experimental course in the field of Internet traffic measurements. Most of the classes will be given in laboratories, with few introductory lessons for each field. Students will be guided to setup experiments, run active or passive measurement tools in real networks, and apply methodologies learned in previous classes. Students will use both passive traffic analysers, and active traffic generators to characterise and load the network. They will face lab sessions of increasing complexity, maturing a critical approach and scientific methodology toward the understanding of complex systems such as the Internet is. During the class, students will also learn basic machine learning methodologies to support the data analysis, following a data driven approach. In this second group of laboratories students will follow a hands-on approach, where the fundamentals of supervised machine learning for classification problem will be introduced and then applied in the context of Internet measurements.
Network Measurements Laboratory is an experimental course in the field of Internet traffic measurements. Most of the classes consist of laboratories, with few introductory lessons for each laboratory. In the first part of the course, students will be guided to set up experiments, run active or passive measurement tools in real networks, and apply methodologies learned in previous classes. Students will use both passive traffic analyzers and active traffic generators to characterize and measure the network's performance. They will face lab sessions of increasing complexity, acquiring a critical approach and scientific methodology toward understanding complex systems such as the Internet. During the second part of the course, students will learn basic machine learning methodologies to support the data analysis, following a data-driven approach. In this second group of laboratories, students will follow a hands-on approach, where the fundamentals of supervised machine learning for classification problems will be introduced and then applied in the context of Internet measurements.
Students will mature a critical view and acquire the fundamentals of machine learning - applied to network measurements. In particular, students will get 1. the knowledge of the analysis of Internet configurations and misconfigurations, and protocols like IP, TCP, UDP, HTTP, DNS. 2. the knowledge of performance related problems in local and wide area networks via delay and throughput measurements 3. the ability to process real traffic traces and to extract information related to both performance and troubleshooting issues from real traffic. 4. the ability to use understand and use properly machine learning tools to solve some classification problems applied to internet traffic analysis. 5. the ability to use modern tools and programming languages like Python and Jupyter Notebook for quick prototyping of high level machine learning problems. The ability to apply the gained knowledge will be verified by the preparation of lab reports, and its discussion during an oral examination at the end of the course.
Students will mature a critical view of computer network technologies and performance and acquire the fundamentals of machine learning - applied to network measurements. In particular, students will get: 1. the knowledge of the analysis of Internet configurations and misconfigurations and protocols like IP, TCP, UDP, HTTP, and DNS. 2. the knowledge of performance-related problems in local, wired, and WiFi networks and wide area networks via delay and throughput measurements 3. the ability to process real traffic traces and extract information related to performance and troubleshooting issues from real traffic. 4. the ability to understand and adequately use machine learning tools to solve some classification problems applied to internet traffic analysis. 5. the ability to use modern tools and programming languages like Python and Jupyter Notebook for quick prototyping of high-level machine learning problems. The ability to apply the gained knowledge will be verified by preparing lab reports and discussing it during an oral examination at the end of the course.
Students must have very good knowledge of the protocols and mechanisms normally used in the Internet. In particular, students must be experts with LAN protocols (Ethernet IEEE 802.3, WiFi IEEE 802.3, CSMA/CD, direct and indirect delivery, LAN interconnections), of network (IP) and transport (TCP/UDP) protocols normally used in the Internet, routing protocols (RIP, OSPF), and application layer protocols (DNS, HTTP, HTTPS, multimedia streaming protocols, etc.). Traffic models will be also used and students must be familiar with basic traffic modeling techniques (Poisson models, Markovian models, hypothesis tests, confidence interval evaluation).
Students must know the protocols and mechanisms normally used on the Internet. In particular, students must be experts with LAN protocols (Ethernet IEEE 802.3, WiFi IEEE 802.11, CSMA/CD, direct and indirect delivery, LAN interconnections), network (IPv4, routing protocols), and transport (TCP/UDP) protocols, and application layer protocols (DNS, HTTP, HTTPS, multimedia streaming protocols, etc.). We will also leverage traffic models, and students must be familiar with basic traffic modeling techniques (Poisson models, Markovian models, hypothesis tests, confidence interval evaluation). There are no particular prerequisites for the second part of the course on Machine Learning applications to network management problems.
Students will work in the Laboratory using PCs running Linux OS. Using Ethernet switches and WiFi access points, students will have to setup experiments configuring LANs. Each group of students will use 3 PCs. Real traffic traces will be used to allow students to deal with more realistic scenarios. Students will go though the following group of laboratories: • Configuration of hosts in Local Area Networks, IP addresses management, subnetting/supernetting (2 hours) • Traffic monitoring using sniffers in LAN: TCP, UDP and HTTP (6 hours) • Performance measurement for file transfers in wired networks (6 hours) • Performance measurement for file transfers in WiFi setup (6 hours) • Performance measurement for file transfers and impact of delay and packet losses (8 hours) • Analysis of traffic traces collected from real networks and development of post-processing tools to extract information out of the raw data (15 hours) • Usage of statistical methods and data mining techniques to find correlations and solve classification problems (15 hours)
Students will work in the Laboratory using PCs running Linux OS. Working in groups, students will set up experiments configuring LANs using Ethernet switches and WiFi access points. Each group of students will use 3 PCs (*) in this first part of the lab. Next, students will use real traffic traces to deal with more realistic scenarios. Students will go through the following group of laboratories: • Configuration of hosts in Local Area Networks, IP addresses management, subnetting/supernetting (2 hours) • Traffic monitoring using sniffers in LAN: TCP, UDP and HTTP (6 hours) • Performance measurement for file transfers in wired networks (6 hours) • Performance measurement for file transfers in WiFi setup (8 hours) • Performance measurement for file transfers and impact of delay and packet losses (8 hours) • Analysis of traffic traces collected from real networks and development of post-processing tools to extract information out of the raw data (15 hours) • Usage of statistical methods and data mining techniques to find correlations and solve classification problems (15 hours)
In this course there is large number of laboratory activities on the topics of communication networks. Students perform these laboratory activities at the the LED laboratories of the Electronic and Communication Department. It will also possible to use your own laptop for laboratories. Each laboratory will be introduced in classes, where the fundamentals of technologies and methodologies will be presented.
This course comprises a large number of laboratory activities on the topics of communication networks. Students perform these laboratory activities at the LED laboratories of the Electronic and Communication Department. It will also be possible to use your own laptop for laboratories, LED labs, or at home. Before each lab session, the fundamentals of technologies and methodologies will be presented in classes to recap the involved technologies.
The teaching material will be made available by the class teacher on the Didattica web portal. Description of the lab experiments will be provided, and reference documentation will be made available to students. No textbook is available. Students must be familiar with Internet protocol and applications which can be found for example in A. Pattavina: Reti di telecomunicazioni, Mc.Graw-Hill (in Italian) or J.F. Kurose, K.W. Ross: Computer Networking: A Top-Down Approach Featuring the Internet, Pearson (in English)
The teaching material will be made available by the class teacher on the Didattica web portal. The material will guide the students with a description of the lab experiments, reference documentation, and theoretical information. No textbook is available. Students must be familiar with Internet protocols and applications, which can be found for example, in -- A. Pattavina: Reti di telecomunicazioni, Mc.Graw-Hill (in Italian) -- J.F. Kurose, K.W. Ross: Computer Networking: A Top-Down Approach Featuring the Internet, Pearson (in English) For the Machine Learning part, we suggest following the book -- Wes McKinney: Python for Data Analysis, O'Reilly (in English- available freely online)
Modalità di esame: Prova orale obbligatoria; Elaborato scritto individuale; Elaborato scritto prodotto in gruppo;
Exam: Compulsory oral exam; Individual essay; Group essay;
During the laboratories, students are required to work in groups. At the end of the course, each group will have to deliver a written report on the laboratory experiences as indicated by the teacher during the course (group report). In total, the report focuses on 8 topics. The report must be delivered in a single PDF file, to be uploaded to the teaching portal by the end of the exam registration deadline. The report will be corrected to verify the correctness of the reported results, and the completeness in the description of the experiments performed. All students in the group will have the same mark, expressed in thirtieths, up to a maximum of 30/30. The group report mark will be valid for all members of the group, and will be valid for two years. Once corrected, the report can be changed only in case all students agree. Each student will also have to prepare and individual report, that have to be uploaded as a single PDF file on the teaching portal by the deadline for the student registration for the exam. The individual report will be discussed during oral examination. Each student will be asked to discuss the content of the reports (both group and individual) during the oral exam. The oral exam is individual, and students will be asked to answer questions about both the practical and theoretical part of the course. The grade for the oral exam must be sufficient to pass the exam, with minimum 18/30 and maximum 30 cum laude. The final vote will be a weighted average between the evaluations of the group laboratory report (70%) and oral examinations (30%). It is possible to get additional points with specific topics reports, or preparing lessons notes to be reused in subsequent years.
Gli studenti e le studentesse con disabilità o con Disturbi Specifici di Apprendimento (DSA), oltre alla segnalazione tramite procedura informatizzata, sono invitati a comunicare anche direttamente al/la docente titolare dell'insegnamento, con un preavviso non inferiore ad una settimana dall'avvio della sessione d'esame, gli strumenti compensativi concordati con l'Unità Special Needs, al fine di permettere al/la docente la declinazione più idonea in riferimento alla specifica tipologia di esame.
Exam: Compulsory oral exam; Individual essay; Group essay;
During the laboratories, students are required to work in groups. At the end of the course, each group will have to deliver a written report on the laboratory experiences indicated by the teacher during the course (group report). In total, the report focuses on eight topics. The report must be delivered in a single PDF file to be uploaded to the teaching portal by the exam registration deadline. The report will be evaluated to verify the correctness of results and the completeness in the description of the performed experiments. All students in the group will have the same mark, expressed in thirtieths, up to a maximum of 27/30. The group report mark will be valid for all group members and will be valid for two years. Once corrected, the report can be changed only if all students agree. Each student will also have to prepare an individual report that must be uploaded as a single PDF file on the teaching portal by the deadline for the student registration for the exam. The individual report will be discussed during the oral examination. During classes and labs, students are invited to anwer questions and discuss the results they obtained, gaining up to 3 points for the final mark . During the oral exam, each student will be asked to discuss the content of the reports (both group and individual). The oral exam is individual, and students will be asked to answer questions about the course's practical and theoretical parts. The grade for the oral exam must be sufficient to pass the exam, with a minimum 18/30 and a maximum 30 cum laude. The final grade will be a weighted average between the evaluations of the group laboratory report (70%) and oral examinations (30%). It is possible to get additional points with specific topic reports or prepare lesson notes to be reused in subsequent years.
In addition to the message sent by the online system, students with disabilities or Specific Learning Disorders (SLD) are invited to directly inform the professor in charge of the course about the special arrangements for the exam that have been agreed with the Special Needs Unit. The professor has to be informed at least one week before the beginning of the examination session in order to provide students with the most suitable arrangements for each specific type of exam.
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