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Engineering mechanics is one of the fundamental branches of science that is important in the education of professional engineers of any major. Most of the basic engineering courses, such as mechanics of materials, fluid and gas mechanics, machine design, mechatronics, acoustics, vibrations, etc. are based on engineering mechanics courses. In order to absorb the materials of engineering mechanics, it is not enough to consume just theoretical laws and theorems—a student also must develop an ability to solve practical problems. Therefore, it is necessary to solve many problems independently. This book is a part of a four-book series designed to supplement the engineering mechanics courses. This series instructs and applies the principles required to solve practical engineering problems in the following branches of mechanics: statics, kinematics, dynamics, and advanced kinetics. Each book contains between 6 and 8 topics on its specific branch and each topic features 30 problems to be assigned as homework, tests, and/or midterm/final exams with the consent of the instructor. A solution of one similar sample problem from each topic is provided. This first book contains seven topics of statics, the branch of mechanics concerned with the analysis of forces acting on construction systems without an acceleration (a state of the static equilibrium). The book targets the undergraduate students of the sophomore/junior level majoring in science and engineering.
Written with computer scientists and engineers in mind, this book brings queueing theory decisively back to computer science.
This book is an introduction to analytical performance modeling for computer systems, i.e., writing equations to describe their performance behavior. It is accessible to readers who have taken college-level courses in calculus and probability, networking, and operating systems. This is not a training manual for becoming an expert performance analyst. Rather, the objective is to help the reader construct simple models for analyzing and understanding the systems in which they are interested. Describing a complicated system abstractly with mathematical equations requires a careful choice of assumptions and approximations. These assumptions and approximations make the model tractable, but they must not remove essential characteristics of the system, nor introduce spurious properties. To help the reader understand the choices and their implications, this book discusses the analytical models in 20 research papers. These papers cover a broad range of topics: processors and disks, databases and multimedia, worms and wireless, etc. An Appendix provides some questions for readers to exercise their understanding of the models in these papers. Table of Contents: Preliminaries / Concepts and Little's Law / Single Queues / Open Systems / Markov Chains / Closed Systems / Bottlenecks and Flow Equivalence / Deterministic Approximations / Transient Analysis / Experimental Validation and Analysis / Analysis with an Analytical Model
This book is an introduction to analytical performance modeling for computer systems, i.e., writing equations to describe their performance behavior. It is accessible to readers who have taken college-level courses in calculus and probability, networking and operating systems. This is not a training manual for becoming an expert performance analyst. Rather, the objective is to help the reader construct simple models for analyzing and understanding the systems that they are interested in.
This book is an introduction to analytical performance modeling for computer systems, i.e., writing equations to describe their performance behavior. It is accessible to readers who have taken college-level courses in calculus and probability, networking, and operating systems. This is not a training manual for becoming an expert performance analyst. Rather, the objective is to help the reader construct simple models for analyzing and understanding the systems that they are interested in. Describing a complicated system abstractly with mathematical equations requires a careful choice of assumptions and approximations. They make the model tractable, but they must not remove essential characteristics of the system, nor introduce spurious properties. To help the reader understand the choices and their implications, this book discusses the analytical models for 40 research papers. These papers cover a broad range of topics: GPUs and disks, routers and crawling, databases and multimedia, worms and wireless, multicore and cloud, security and energy, etc. An appendix provides many questions for readers to exercise their understanding of the models in these papers.
This textbook provides an introduction to common methods of performance modeling and analysis of communication systems. These methods form the basis of traffic engineering, teletraffic theory, and analytical system dimensioning. The fundamentals of probability theory, stochastic processes, Markov processes, and embedded Markov chains are presented. Basic queueing models are described with applications in communication networks. Advanced methods are presented that have been frequently used in recent practice, especially discrete-time analysis algorithms, or which go beyond classical performance measures such as Quality of Experience or energy efficiency. Recent examples of modern communication networks include Software Defined Networking and the Internet of Things. Throughout the book, illustrative examples are used to provide practical experience in performance modeling and analysis. Target group: The book is aimed at students and scientists in computer science and technical computer science, operations research, electrical engineering and economics.
Based on the author's experience in industry, this book focuses on simple techniques for solving everyday problems in systems design and analysis. All techniques are covered in a non-mathematical way, so that no statistics expertise is necessary.
This is a short, occasionally funny, book on how to solve and avoid application and/or computer performance problems. I wrote it to give back the knowledge, insights, tips, and tricks I was given over the last 25 years of my computing career. It shows practical ways to use key performance laws and gives well tested advice on how (and when) to do performance monitoring, capacity planning, load testing, and performance modeling. It works for any application or collection of computers because it teaches you how to decipher whatever meters they give you and how to discover more about those meters than the documentation reveals. This book covers the things that will always be true no matter what technology you are using. It will continue to be useful 20 years from now when today's technology, if it runs at all, will look as quaint as a mechanical cuckoo clock. There is no complex math required; yet it allows you to easily use some fairly advanced techniques. Simple arithmetic, and a spreadsheet program, is all that is required of you. Lastly, it helps with the human side of performance. It shows you how to get the help you need and how to present your findings (good or bad) all the way up to the CIO level.
This book is an introduction to analytical performance modeling for computer systems, i.e., writing equations to describe their performance behavior. It is accessible to readers who have taken college-level courses in calculus and probability, networking and operating systems. This is not a training manual for becoming an expert performance analyst. Rather, the objective is to help the reader construct simple models for analyzing and understanding the systems that they are interested in. Describing a complicated system abstractly with mathematical equations requires a careful choice of assumptions and approximations. They make the model tractable, but they must not remove essential characteristics of the system, nor introduce spurious properties. To help the reader understand the choices and their implications, this book discusses the analytical models for 30 research papers. These papers cover a broad range of topics: processors and disks, routers and crawling, databases and multimedia, worms and wireless, multicore and cloud, etc. An appendix provides many questions for readers to exercise their understanding of the models in these papers. Table of Contents: Preface / Preliminaries / Concepts and Little's Law / Single Queues / Open Systems / Markov Chains / Closed Systems / Bottlenecks and Flow Equivalence / Deterministic Approximations / Transient Analysis / Experimental Validation and Analysis / Analysis with an Analytical Model / Bibliography / Author's Biography
This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications.