Fall 2019
Update: This course becomes a "core" course from AY2017, accordingly, its code is changed, from CNA04 to CNC06.
Teaching staff
Professor Anh T. Pham, pham, office hours: by appointment @306C
Assistant Instructor: Thanh Pham and Anh-Tuan H. Bui
Course syllabus (pdf, get it here)
Prerequisites: basic knowledge of (1) computer networks and (2) probability theory. Though not required, students are recommended to take CNC01 before (or in parallel) this course.
Lecture meeting: Tue-1/2, Fri-1/2 (except holidays, and some re-defined days, check the detail schedule)
Lecture room: S9
Course website: http://web-int.u-aizu.ac.jp/~pham/pe/ (Internal access only)
Course GoogleDrive link: click here.
All course materials, including handouts, HW, lab assigments, course syllabus etc. are kept and shared in the Gdrive.
The objectives of the course include fundamental of data networks, network algorithms and performance analysis. After the course, students are expected to understand what and how to evaluate the performance of a network as well as how and why different networks algorithms are designed. To take the course, students are expected to have a good mathematical background, basic knowledge of computer networks and the probability theory. Students are strongly recommended to take either CNC01 (grad. school) or N1 (undergrad school) prior (or at least, in parallel) to this course.
For all lecture handouts and HW, download from the Course Google Drive (link above).
Lecture 1: Performance analysis methods (mathematical model, simulation, emulation, practical implementation) and metrics (capacity, throughput, goodput, Loss probability, delay, queue length)
Lecture 2: Network review, layering
architecture (TCP basics, if possible).
Lecture 3: Point to point protocols and
methods: Congestion control in TCP
Lecture 4: Point to point protocols and
methods: Error recovery methods, ARQ.
Lecture 5: Multiple access networks: Aloha, Carrier Sensing. Chapter 4, section 4.2, 4.4, Textbook ref. 3, chapter 5 (section 5.3, others for self -reading)
Lecture 6: MAC in Wireless networks. Ref. 4, chapter 4 (sects 4.1 to 4.4), Chapter 6, section 6.3.2 of ref. 3.
Lecture 7: Probability theory, Basic concepts. Textbook ref. 5 (online)
Lecture 8-9: Random Processes (Bernoulli and Poisson processes) Textbook ref. 5 (online)
Lecture 10: Markov chains. Textbook ref. 5 (online)
Lecture 11: Intro to Queuing model and Little’s theorem. Chapter 3: 3.2
Lecture 12: M/M/1, M/M/m; Chapter 3: 3.3, 3.4
Lecture 13: Loss Queuing Systems: M/M/∞, M/M/m/m, and M/G/1 system Chapter 3: 3.4, 3.5
Lecture 14: Loss network vs. queuing network
The detail lecture and assgiment schedule can be found in the course syllabus (shared in the Gdrive)
"Data networks" 2/E by D. Bertsekas and R. Gallager. Note: You can borrow this textbook from the Univ. Lib. Each chapter is downloadable (legal, shared by authors) from this website.
High performance TCP/IP Networking by M. Hassan and R. Jain
“Computer Networking: A Top-Down Approach Featuring the Internet”, by Kurose (any edition is OK)
“Computer Networks” (4th ed.) by A. S. Tanenbaum
“MIT Intro. to Probability Theory” (free, available online)
Network Simulation Experiments Manual by E. Aboelela
The Art of Computer Systems Performance Analysis – by Raj Jain
HW assignments (H, 100-band): every week: H = arithmetic mean of all HWs
Project (P, 100-band): two mini projects + one course project
Each mini project: 30%
Course project: 40%
No mid-term or final examination
Final grade is geometric mean of H and P
Anh T. Pham, 2011--2019