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Some of the topics addressed, such as risk and team practices, are rather new for many in the business. I liked the approach proposed in this book, which seemed to me above and beyond the current state-of-the-art.

Topics in reliability correlated failures, root-cause analysis and scheduling overload management, load balancing, architectural issues, etc.

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Many of the issues related to automated monitoring and incident detection could lead in the future to better technology and much innovation, so I liked the prominence given to these topics in this book.

What I didn't like I thoroughly disliked the statements claiming by omission that Google has invented most of the concepts presented in the book, which of course in the academic world would have been promptly sent to the reject pile.

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I'll skip the discussion about who is the originator of the term SRE, and focus on the meat of this statement. By omission, it makes the reader think that Google, through its Ben Treynor Sloss, is the first to understand the importance of reliability for datacenter-related systems. In fact, this has been long-known in the grid computing community.

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Of course, this notion has been explored for the general case of services much earlier The list of concepts actually not invented at Goog but about which the book implies to the contrary goes on and on I also did not like some of the exaggerated claims of having found solutions for the general problems.

Much remains to be done, as hiring at Google in these areas continues unabated. There's also something called computer science, whose state-of-the-art indicates the same. The third edition has been extensively revised and updated from the old second edition The emphasis is on why things are done rather than on exactly how to do them.

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If you already know something about the subject, then working through this book will probably deepen your understanding. The book begins by identifying four general classes of data analysis problem, and uses elementary probability along with Bayes' theorem to explain exactly what each involves. The next two chapters use some simple distributions to illustrate these ideas.

Further chapters discuss the Monte Carlo method briefly , least-squares fitting in some detail , and the problem of determining a distribution function from data. The book ends with an interesting pair of chapters on entropy: Crucial in the analysis and design of control systems, this book presents a unified approach to robust stability theory, including both linear and nonlinear systems, and provides a self-contained and complete account of the available results in the field of robust control under parametric uncertainty.

Monitoring Distributed Systems

These results are elegant and their direct application to control system design is illustrated by numerous examples, including MATLAB software. Covers the Generalized Kharitonov Theorem, the theory of disk polynomials, the extremal properties of interval systems, the calculation of the real parametric stability margin, and design problems under simultaneous parametric and nonparametric uncertainty.

For electrical, mechanical, nuclear, chemical, and aerospace engineers. Free Engineering books page 4.