Teaching

15-858 Section C

Fall 2018

Algorithms and Analysis for Large-Scale Cloud Computing Systems

Time: Tuesday and Thursday 10:30 AM - 11:50 AM
Location: GHC 4303
Units: 12
Instructor: Weina Wang weinaw “at” cs “dot” cmu “dot” edu
Office hours: anytime, please send an email first
Prerequisite: undergraduate probability, mathematical maturity in general

Course Description

Large-scale cloud computing systems are at the heart of today’s emerging technologies. A key challenge in advancing the state-of-the-art is to design systems with stringent performance guarantees such as high-throughput and low-delay. To tackle this challenge, it is important to understand how algorithms in various components of the system have an impact on the performance. This course introduces theories and techniques from applied probability and stochastic processes for building models and analyzing algorithms. The goal is to address questions such as: How should computation tasks be assigned to the large number of servers in a cloud computing system? How should the bandwidth in a datacenter network be allocated to achieve fast data transfer? How should data be distributed and how does that affect the efficiency of computation?

Topics covered include:

Learning Objectives

After successfully completing this course, you will

Readings

Course Schedule

Date Topics
[Sep 4] Introduction: A datacenter from the modeling perspective
[Sep 6] Discrete-time Markov chain (DTMC) (References: Chapter 3.3 of Srikant and Ying’s book, Chapter 2 & 6 of Hajek’s book, Chapter 5 of Gallager’s book/notes)
[Sep 11] Foster-Lyapunov stability criterion with application in load-balancing
[Sep 13] Applying Foster-Lyapunov to the giant switch abstraction of datacenter networks
[Sep 18] Moment bounds given by the drift
[Sep 20] The drift method (I)
[Sep 25] The drift method (II)
[Sep 27] The drift method (III)
[Oct 2] Continuous-time Markov chain (CTMC)
[Oct 4] No class
[Oct 9] Extending Foster-Lyapunov and the drift method to CTMC
[Oct 11] Course project presentation
[Oct 16] Course project presentation
[Oct 18] Mean-field analysis: Cavity method and differential equations method
[Oct 23] Mean-field analysis: Kurtz’s theorem example
[Oct 25] Mean-field analysis: Kurtz’s theorem proof and more examples
[Oct 30] Mean-field analysis: Interchange of limits, asymptotic independence
[Nov 1] Mean-field analysis: asymptotic independence, neural network example
[Nov 6] No class
[Nov 8] Data management in datacenters: Data locality and data transfer
[Nov 13] A connection-level model for data transfer, multi-commodity flow algorithms
[Nov 14] (Makeup class) Open problems in modern large-scale computing systems
[Nov 15] Course project presentation
[Nov 20] Course project presentation
[Nov 27] Course project presentation
[Nov 29] Course project presentation

Evaluation

Homework: 60%
You will have homework every other week.

Course project: 40%
You will choose a topic of your interest and read up papers in the literature. I will suggest some topics and papers. But you are free to choose any topic that you find particularly interesting as long as it is related to this course. You can work by yourself or together with another student. Your team will submit a brief report every four weeks to document your progress, and a final report at the end of the semester. Of course you are also welcome to have discussions with me anytime. Your team will also give a 40 min presentation to show your findings. You are encouraged to identify and solve problems beyond the existing work in the literature – there will be bonus in your grade and I am happy work with you if desired to help you submit your work to a conference.

Course Policies