15-857: Analytical Performance Modeling & Design of Computer Systems

Cross-listed with Tepper: 47-774 & 47-775. 12 Units.

Satisfies THEORY CORE requirement for CSD PhDs.

Classes: M,W 1:25 p.m. - 2:45 p.m., Room: GHC 4307

Recitation: F 1:25 p.m. - 2:45 p.m., Room: GHC 4307

CLASS STARTS AUGUST 30, 2021

www.cs.cmu.edu/~harchol/Perfclass/class21fall.html

We will closely follow the textbook: Performance Modeling and Design of Computer Systems .

Probability is a prerequisite for this class. Upon registration, you will receive review chapters on probability, which you are responsible for.

INSTRUCTORS:


Prof. Mor Harchol-Balter

Prof. Weina Wang

TA Jalani Williams

TA Mik Zlatin

Click here: ANNOUNCEMENTS and HOMEWORKS

Office Hours:

DESCRIPTION:

In designing computer systems one is usually constrained by certain performance requirements and limitations. For example, one might need to guarantee a response time SLA or certain throughput requirement, while at the same time staying within a power budget or cost budget. On the other hand, one often has many choices: One fast disk, or two slow ones? More memory, or a faster processor? A fair scheduler or one that minimizes mean response time? For multi-server systems, one can choose from a wide array of load balancing policies, a wide array of migration policies, capacity provisioning schemes, power management policies ... The possibilities are endless. The best choices are often counter-intuitive. Ideally, one would like to have answers to these questions before investing the time and money to build a system. This class will introduce students to analytic stochastic modeling with the aim of answering the above questions.

Topics covered include:

Throughout, the theory developed will be applied to a wide array of computer systems design problems including the design of efficient data centers, web servers, DBMS, disks, call centers, routers, and supercomputer centers.

The techniques studied in this class are useful to students in Computer Science, ECE, Mathematics, ACO, Tepper, Statistics, MLD, and Engineering. This course is packed with open problems -- problems which if solved are not just interesting theoretically, but which have huge applicability to the design of computer systems today.

For a more detailed description see the Table of Contents of the book.

PREREQUISITES:

We assume a reasonable background in probability, such as that covered in an Undergraduate Probability class. Specifically, we assume a knowledge of continuous and discrete distributions, conditional probability, conditional expectation, and higher moments. Chapter 3 of our textbook summarizes most of the assumed material. Alternatively, you can get a much better feel for the assumed material by reading Chapters 2 through 10 of the Undergraduate Probability Notes, in the "Probability for Computer Scientists" text that was mailed to you. We also expect you to know basic calculus and nested integrals, as are covered in Chapter 2 of the "Probability for Computer Scientists" text. There is an assessment provided on the first day of class to make it clear to you if you have the prerequisites with respect to undergraduate probability and calculus.

GRADING:

COLLABORATING vs. CHEATING and other RULES:

You will receive regular homework problems. These will be difficult. Start immediately so that you can take full advantage of office hours. You will find office hours very helpful! Some of these homework problems will be repeated from previous years. The reason is that we have made up all the problems ourselves and it takes a very long time to think up good problems. Do not ask people who took this course in previous years to help you with the homeworks. This is considered cheating and will be reported to the dean. On the other hand, we strongly encourage you to collaborate with your current classmates to solve the homework problems after you have tried solving them by yourself. Each person must turn in a separate writeup. You should note on your homework specifically which problems were a collaborative effort and with whom.

Please, no laptops during class. You will get a handout on which you can write your notes. Also, please no eating/drinking during class until COVID-19 improves. There will be a 3-minute break during most classes, to give you a chance to eat a quick snack or get a drink of water.

PRIOR COURSE EVALUATIONS:

Prior course evaluations average 4.8/5.0. To see all FCEs for the instructors
Click Here .

CLASS SPONSOR: