Course Description:
The course introduces basic concepts of performance evaluation and analytical modeling of modern computer systems and networks. The topics include: measurement techniques, monitoring tools, statistical analysis, simulation, analytic models, Queuing theory, benchmarks, and performance evaluation problems.
By the end of this course students are well-prepared with theoretical and practical knowledge for experimental design, measurement, simulation, and modeling of modern computer and communication systems.
Text Book:
Raj Jain, The Art of Computer Systems Performance Analysis: Techniques, Experimental Design, Measurement, Simulation and Modeling,
John Wiley & Sons, Inc., New York, NY, 1991.
The course introduces basic concepts of performance evaluation and analytical modeling of modern computer systems and networks. The topics include: measurement techniques, monitoring tools, statistical analysis, simulation, analytic models, Queuing theory, benchmarks, and performance evaluation problems.
By the end of this course students are well-prepared with theoretical and practical knowledge for experimental design, measurement, simulation, and modeling of modern computer and communication systems.
Text Book:
Raj Jain, The Art of Computer Systems Performance Analysis: Techniques, Experimental Design, Measurement, Simulation and Modeling,
John Wiley & Sons, Inc., New York, NY, 1991.
Lecture Notes:Lecture notes will be available in PPT and PDF format.
Lecture | Topic | PPT Notes |
#1 (M) | Introduction (Course Admin, Objectives, Common Mistakes) | |
#2 (W) | Introduction (Systematic Approach, Example) Selection of Techniques and Metrics | |
#3 (M) | Workloads: Types, Selection, and Characterization | |
#4 (W) | Workloads: Selection and Characterization | |
#5 (W) | Monitors | |
#6 (M) | The Art of Data Presentation; Ratio Games | |
#7 (W) | Summarizing Measured Data | |
#8 (M) | Comparing Systems Using Sample Data | |
#9 (W) | Simple Linear Regression Models | |
#10 (M) | Other Regression Models | |
#11 (W) | Experimental Design | |
#12 (M) | Experimental Design | |
#13 (W) | 2^(k-p) Factorial Design | |
#14 (M) | One Factor Experiments | |
#15 (W) | Midterm Exam | |
#16 (M) | One Factor Experiments Two Factor Full Factorial Design without Replications | |
#17 (W) | Two Factor Full Factorial Design with Replications; Full Factorial Design with k Factors | |
#19 (W) | Introduction to Simulation | |
#20 (M) | Random Number Generation | |
#21 (W) | Testing Random Number Generators, Random-Variate Generation | |
#22 (M) | (Random Variate Generation) Introduction to Queueing Theory | |
#23 (W) | Analysis of a Single Queue (M/M/1) | |
#24 (M) | Analysis of a Single Queue (M/M/m) Queueing Networks | |
#25 (W) | Operational Laws | |
#26 (M) | Mean-Value Analysis and Related Techniques |
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