Advanced Network Management (Spring 2024)

Course Instructor

Dan Pei
Associate Professor
Department of Computer Science and Technology
Email: peidan(at)tsinghua(dot)edu(dot)cn
Office: East Main Building 9-319

Class Time and Location

Class Time: Wednesday 9:50am – 12:15pm (please see detailed schedule in the course syllabus below) 
Location: 三教3507 (Room 3507, Teaching Building 3)

Project: Time Series Forecasting Paper Reading and Reproduction

The entire class collaboratively completes a time series forecasting benchmark. Each student will be assigned papers related to time series forecasting. After carefully reading the papers, students are required to reproduce the code and complete a benchmark report.

Grading Policies

  1. Attendance: 10%
  2. Paper Reading & Presentation: 20%
  3. Algorithm Reproduction & Project Presentation: 40%:
  4. Report Writing: 30%

Syllabus:

Week Date Topic Techniques Case Studies Slides
1 Feb 28 (9:50am-12:15pm) Introduction 1. Introduction to the course
2. Introduction to AIOps
3. Brief Introduction to assignments and projects
4. ANM website		 Week 1
2 Mar 6 (9:50am-12:15pm) Visualization 1. Student Self-Introduction
2. Storytelling with Visualization
3. Introduction to Visualization		 Week 2
3 Mar 13 (9:50am-12:15pm) Correlation 1. Video Streaming Basics
3. Correlation and Regression
4. Information Gain		 2. SIGCOMM 2011 Case Study Week 3
4 Mar 20 (9:50am-12:15pm) Decision Tree 1. Decision Trees
3. Feature Selection
4. Accuracy_CV_Overfitting		 2. SIGCOMM 2013 Case Study Week 4
5 Mar 27 (9:50am-12:15pm) KPI Anomaly Detection with Transfer Learning 1. Time-Series Anomaly Detection Service at Microsoft
2. Time series algorithms Tutorial
3. Random Forest
4. Deep Learning
5. Deep Generative Model
6. Similarity
7. Clustering
8. Deep Sequence Learning
9. Transfer Learning		 10. Case study: Opprentice (IMC’ 15)
11. Case study: Donut (WWW’ 18) & ROCKA (IWQoS’18)
12. Case study: OmniAnomaly (KDD’ 19) & CTF (INFOCOM’ 21)		 Week 5
6 Apr 3 (9:50am-12:15pm)  Week 6
7 Apr 10 (9:50am-12:15pm) Week 7
8 Apr 17 (9:50am-12:15pm) Foundation Model for KPI Forecasting

1. Transformer
4. CNN

2. Case Study: SimMTM (NeurIPS’ 23)
3. Case Study: TimesNet (ICLR’ 23)
5. Case Study: STEP (KDD’ 22)

Week 8
9 Apr 24 (9:50am-12:15pm) Week 9
10 May 1 (No class due to school holidays)
11 May 8 (9:50am-12:15pm) Multivariate Time Series Clustering and Outlier Detection

1. STGNN
2. Towards Temporal-Spatio Foundation Models in Cyberspace

3. OmniCluster: WWW 2022

Week 11
12 May 15 (9:50am-12:15pm) Causal Discovery among Time Series

1. Hiearchical Clustering
3. Graph Algorithms: Random Walk and Page Rank
5. Association Mining

2. CoFlux (IWQoS 2019)
4. MonitorRank
6. DEXA’22

Week 12
13 May 22 (9:50am-12:15pm) Week 13
14 May 29 (9:50am-12:15pm) Causal Inference on a Graph 1. Microservices
2. Causal Inference
4. Agent		 3. CIRCA (KDD’22)
4. LatentScope
6. G-RCA		 Week 14
15 Jun 5 (9:50am-12:15pm)
16 Jun 12 (9:50am-12:15pm) Project Presentation

Course Information

Course Number
80240663

Credit
3

Required text
None.

Reference texts
《Data Science for Business–What you need to know about data mining and data-analytical thinking》Foster Provost & Tom Fawcett

《MIT 6.S191 Introduction to Deep Learning 》 with video and slides.

《Site Reliability Engineering –How Google Runs Production Systems》, by Betsy Beyer, Chris Jones, Jennifer Petoff & Niall Richard Murphy

Prerequisites
You are expected to be familiar with at least one programming language.

Course Assistant

Zhe Xie
Email: xiez22(at)mails(dot)tsinghua(dot)edu(dot)cn

Previous Courses

 
 
 
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