This course, led by Prof. Krishna Moorthy Sivalingam from the Department of Computer Science and Engineering at IIT Madras, focuses on the performance evaluation of computer systems. The curriculum includes:
Students will learn through case studies, simulations, and various modeling techniques, providing a comprehensive understanding of performance evaluation methodologies.
This module introduces the fundamental concepts of performance evaluation in computer systems. It covers the importance of assessing system efficiency and effectiveness in various contexts.
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This module discusses common mistakes made during performance evaluation. It emphasizes the significance of correct methodologies and techniques to avoid pitfalls.
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This module focuses on selecting appropriate techniques and metrics for performance evaluation. Understanding which tools to utilize based on the system's requirements is crucial.
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This case study module illustrates the practical application of techniques and metrics in performance evaluation. Real-world scenarios will be analyzed to enhance understanding.
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This module introduces random variables and probability distributions, foundational concepts in performance evaluation. Understanding these concepts is vital for accurate analysis.
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This module delves into the first types of probability distributions, providing insight into their characteristics and applications in performance evaluation.
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This module continues the exploration of probability distributions, focusing on additional types and their relevance in evaluating computer system performance.
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This module further explores probability distributions, focusing on advanced distributions and their applications in performance evaluation.
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This module covers stochastic processes, explaining their role in performance evaluation and the mathematical foundations that support them.
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This module focuses on Markov Chains, their properties, and their applications in performance evaluation of computer systems.
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This module examines the Slotted Aloha protocol model and its relation to discrete-time birth-death processes, emphasizing their significance in performance evaluation.
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This module introduces continuous-time Markov chains and queuing theory, highlighting their importance in modeling performance metrics in computer systems.
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In this module, we continue our exploration of queuing theory, focusing on its applications in computer systems. Queuing theory is essential for understanding how resources are allocated and utilized in processing tasks. We will cover:
By the end of this module, students will have a deeper understanding of how queuing theory can optimize system performance.
This module introduces the second part of queuing theory, emphasizing more advanced concepts. Students will learn about:
Through examples and case studies, we aim to highlight the importance of these concepts in efficient system design.
This module continues the discussion on queuing theory with a focus on more intricate queuing models. Topics covered will include:
The goal is to provide students with practical insights into how these models can be applied in various scenarios.
In this module, we delve deeper into queuing theory, with a focus on analyzing complex systems. Subjects include:
The emphasis will be on understanding how to model and evaluate system performance effectively.
This module expands on previous concepts to include further applications of queuing theory. Key topics include:
Students will learn how to apply theory to practical problems, enhancing their analytical skills.
This module covers the advanced aspects of queuing theory focusing on the last few concepts. Subjects include:
Through discussions and examples, students will understand the complexities of real-world systems.
This module presents additional complexities in queuing theory, including topics like:
Students will gain practical insights into managing queues effectively in varied scenarios.
This module introduces queuing networks, focusing on their structure and functionality. Key topics include:
Students will learn how to model complex systems effectively, using queuing networks.
This module covers the Slotted Aloha Markov model, focusing on its significance in networking. Key points include:
Students will explore practical examples to appreciate the model's impact on network design.
This module introduces simulations as a tool for analyzing queuing systems. Topics include:
Students will learn how to implement simulations to derive insights from queuing systems.
This module continues the discussion on simulations, focusing on more complex scenarios. Key topics include:
Students will gain deeper insights into the complexities of simulation applications in queuing analysis.
This module concludes the series on simulations in queuing systems, discussing comprehensive techniques. Key points include:
Students will be equipped with knowledge and skills to apply simulations effectively in real-world scenarios.
This module delves into the fundamental operational laws that govern the performance of computer systems. Understanding these laws is crucial for evaluating and optimizing system performance.
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This module continues the exploration of operational laws, expanding on the previous lectures to include more complex scenarios in performance evaluation. Students will learn about:
In this module, students will explore the concept of open and closed queuing networks, essential for understanding how tasks flow through systems. Key aspects include:
This module introduces the Approximate Mean Value Analysis (MVA) method, a powerful technique for evaluating the performance of queuing networks. The module covers:
In this module, we focus on the Convolution Algorithm, a critical method for solving complex queuing problems. The module will cover:
This module continues the exploration of the Convolution Algorithm, providing deeper insights and more complex examples to solidify understanding. It will address:
This module discusses load-dependent service centers, an essential concept for understanding how varying loads affect performance metrics. Key topics include:
This module focuses on hierarchical decomposition techniques, which are vital for managing complex systems by breaking them down into simpler components. The coverage includes:
This module introduces Balanced Job Bounds, focusing on their role in performance evaluation. Participants will learn about:
This module covers confidence intervals for proportions and introduces the principles of experimental design. Participants will explore:
This module introduces the 2k factorial design, a vital method for conducting experiments efficiently. It will cover:
This module continues the exploration of factorial designs by introducing the 2k-p fractional factorial design. Topics addressed will include:
This module introduces the fundamental concepts of discrete-event simulations. Students will learn how to model systems using discrete events, which are essential for analyzing complex systems. Key topics will include:
The knowledge gained will be applied to various fields, enhancing understanding of system performance and optimization.
Continuing from the previous module, this session delves deeper into programming aspects of discrete-event simulations. Students will focus on:
Participants will gain hands-on experience, enabling them to create and analyze their own simulation scenarios effectively.
This module covers the third part of discrete-event simulations, emphasizing more complex scenarios and methodologies. Key areas of focus include:
Students will learn how to ensure their simulations accurately reflect real-world systems through rigorous analysis and testing methodologies.
This module introduces Petri Nets, a powerful mathematical modeling tool used for describing and analyzing systems. The key topics include:
Students will explore how Petri Nets can be used to model concurrent, asynchronous, distributed, and parallel systems effectively.
Building upon the previous module, this lecture expands on the intricacies of Petri Nets. Topics will include:
This session will enable students to deepen their understanding of system dynamics and how to leverage Petri Nets for effective modeling.
This module concludes the study of Petri Nets by examining advanced concepts and applications. Topics covered include:
Students will learn how to model more complex systems and use Petri Nets to evaluate performance in various real-world scenarios.