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Distributed Algorithms (The Morgan Kaufmann Series in Data Management Systems), by Nancy A. Lynch
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In Distributed Algorithms, Nancy Lynch provides a blueprint for designing, implementing, and analyzing distributed algorithms. She directs her book at a wide audience, including students, programmers, system designers, and researchers.
Distributed Algorithms contains the most significant algorithms and impossibility results in the area, all in a simple automata-theoretic setting. The algorithms are proved correct, and their complexity is analyzed according to precisely defined complexity measures. The problems covered include resource allocation, communication, consensus among distributed processes, data consistency, deadlock detection, leader election, global snapshots, and many others.
The material is organized according to the system model―first by the timing model and then by the interprocess communication mechanism. The material on system models is isolated in separate chapters for easy reference.
The presentation is completely rigorous, yet is intuitive enough for immediate comprehension. This book familiarizes readers with important problems, algorithms, and impossibility results in the area: readers can then recognize the problems when they arise in practice, apply the algorithms to solve them, and use the impossibility results to determine whether problems are unsolvable. The book also provides readers with the basic mathematical tools for designing new algorithms and proving new impossibility results. In addition, it teaches readers how to reason carefully about distributed algorithms―to model them formally, devise precise specifications for their required behavior, prove their correctness, and evaluate their performance with realistic measures.
- Sales Rank: #594120 in Books
- Brand: Brand: Morgan Kaufmann
- Published on: 1996-03-15
- Original language: English
- Number of items: 1
- Dimensions: 9.52" h x 1.80" w x 7.70" l, 3.60 pounds
- Binding: Hardcover
- 904 pages
Features
- Used Book in Good Condition
Review
Shows students, programmers, system designers and researchers how to design, implement, and analyze distributed algorithms. Familiarizes readers with the most important problems, algorithms, and impossibility results in the area.
Provides the basic mathematical tools for designing new algorithms and proving new impossibility results. Teaches how to reason carefully about distributed algorithms--to model them formally, devise precise specifications for their required behavior, prove their correctness, and evaluate their performance with realistic measures.
Features:
* The most significant algorithms and impossibility results in the area, all in a simple automata-theoretic setting.
* The algorithms are proved correct, and their complexity analyzed according to precisely-defined complexity measures.
* The problems covered include resource allocation, communication, consensus among distributed processors, data consistency, deadlock detection, leader election, global snapshots, and many others.
The material is organized according to the system model -- first, according to the timing model, and then, by the interprocess communication mechanism. The material on system models is isolated into separate chapters for easy reference. -- Book Description
This is the finest texbook it has been my pleasure to review, and I strongly recommend it to both the specialist and the merely interested reader. The real contribution comes from the presentation of so many algorithms in a common and usable style. It does for distributed algorithms what Knuth Volume I did for sequential ones. -- Julian Padget (Mathematical Reviews, January 1997)
From the Back Cover
In Distributed Algorithms, Nancy Lynch provides a blueprint for designing, implementing, and analyzing distributed algorithms. She directs her book at a wide audience, including students, programmers, system designers, and researchers.
Distributed Algorithms contains the most significant algorithms and impossibility results in the area, all in a simple automata-theoretic setting. The algorithms are proved correct, and their complexity is analyzed according to precisely defined complexity measures. The problems covered include resource allocation, communication, consensus among distributed processes, data consistency, deadlock detection, leader election, global snapshots, and many others.
The material is organized according to the system model―first by the timing model and then by the interprocess communication mechanism. The material on system models is isolated in separate chapters for easy reference.
The presentation is completely rigorous, yet is intuitive enough for immediate comprehension. This book familiarizes readers with important problems, algorithms, and impossibility results in the area: readers can then recognize the problems when they arise in practice, apply the algorithms to solve them, and use the impossibility results to determine whether problems are unsolvable. The book also provides readers with the basic mathematical tools for designing new algorithms and proving new impossibility results. In addition, it teaches readers how to reason carefully about distributed algorithms―to model them formally, devise precise specifications for their required behavior, prove their correctness, and evaluate their performance with realistic measures.
About the Author
About the author: Nancy A. Lynch is a professor of electrical engineering and computer science at MIT and heads MIT's Theory of Distributed Systems research group. She is the author of numerous research articles about distributed algorithms and impossibility results, and about formal modeling and verification of distributed systems.
Most helpful customer reviews
5 of 5 people found the following review helpful.
Good textbook, but hard to use for self-study
By Ned Ruggeri
I am happy with the seriousness of the book. This book is written in a very formal, mathematical style. I am happy with the seriousness of the book, and the breadth of material it covers. I like that it organizes material by network models.
What I really dislike about this book is that it provides very little intuition for the algorithms it presents. The book will pose a problem, then present a distributed algorithm that solves the problem, with a proof of correctness. It would be very helpful if the book presented naive attempts at solutions, explaining why they didn't work, and how the final solution addresses and avoids those failures.
This would be helpful because with many of the algorithms Prof. Lynch presents, I waste a lot of time trying to figure out why a simpler approach wouldn't work. I usually do convince myself why the presented solution truly addresses failings of my naive solutions, but it takes a long time. A second reason this would be helpful is that it would help explain the presented algorithms; I spend a lot of time scratching my head, trying to figure out how a complicated algorithm works. If I could see a simpler, easier to understand, but not entirely correct "partial solution", I could grok that, and then slowly understand a series of evolutionary steps as we improve that algorithm toward a full solution.
This makes the book difficult for use for self-study. In a classroom setting, I think you could get more of the intuition from a lecturer, or from having someone to ask questions too. That said, I do feel a great sense of achievement as I make progress through the book; Prof. Lynch doesn't rob you of a sense of discovery by taking you through every baby step. But I also would be happier if there were left blanks to fill in and if I could make faster progress through the text.
I would still recommend the book; this does appear to be the definitive text, and reading it I feel like I am (slowly) gaining a mastery of this subject. My feedback is really more motivated by a general frustration with many texts written in what I feel to be an overly formal, unintuitive style.
1 of 1 people found the following review helpful.
Highly Recommended
By Gregory Woodhouse
In this book, Nancy A. Lynch provides a very thorough account of the theory of distributed algorithms, using elementary (if sometimes intricate) methods based on I/O automata. Algorithms are presented both informally, and formally using automata. Along the way, a number of bounds and impossibility results are presented. Each chapter includes a useful section providing pointers to the research literature, additional reading, and further bibliographic notes.
The book is divided into three sections: synchronous algorithms, asynchronous algorithms, and partially synchronous algorithms (asynchronous algorithms with timing constraints). The first two sections are further divided into shared memory algorithms and network algorithms. Each section is interesting in its own right, of course, but the early sections introduce theoretical tools that are exploited throughout. The final section, on partially synchronous algorithms, was more of a survey than the previous sections. This is perhaps inevitable, because partially synchronous algorithms are a research topic, and not nearly as much is known as is known in the case of purely asynchronous algorithms.
One disappointment from my point of view is that there was no use of formal methods (temporal logic, process logics, and such). Instead, proofs generally used straightforward arguments using simulation arguments, invariant assertions, and the like). I would have liked to have seen how formal methods could have been brought to bear on the problems discussed in this book. On the other hand, this is also an advantage because the mathematical prerequisites are kept to a minimum. That does not, however mean this book is an easy read. It can be quite challenging at times.
28 of 33 people found the following review helpful.
The original printed book is great, but the technical quality of the Kindle edition is extremely poor
By JS
This book is a classic, and I was excited when I learned that this excellent reference work is also available as a Kindle edition.
Unfortunately, the technical quality of the Kindle version is extremely poor. In particular, many parts of it are very difficult to follow because of several technical errors that have been introduced in the conversion of the printed book into Kindle edition.
The Kindle edition is barely useful as a reference if you already have read the printed book, and just want to quickly look up some definitions or references. Trying to read any non-trivial fragment of the Kindle version is a painful experience.
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I am giving here just some examples of the issues that should have been easy to spot before publishing the Kindle version of the book.
Throughout the book, there are numerous strange errors in mathematical formulas. There are confusing mistakes such as using $o(n)$ instead of $O(n)$, or replacing the floor notation with brackets "[...]", or replacing the $\ge$ symbol with text "VI". In many places, the book uses $\epsilon$ instead of $\in$, "U" instead of $\cup$, "V" instead of $\vee$, "." instead of $\cdot$, etc.
There are lots of alignment issues; superscripts and subscripts are often lost. Spacing is wrong, for example, there is often "O (n log n)" instead of "O(n log n)" or "O (logn)" instead of "O(log n)". Hyphens and minus signs are wildly mixed up even within a single paragraph of text. In general, you can expect all kinds of mistakes that happen when you try to apply OCR to mathematical formulas, without carefully proofreading the end result.
Many text fragments - more complicated formulas, algorithm listings, etc. - seem to be low-resolution scanned images. There is a lot of noise in the figures, and the font size in the figures is tiny, making subscripts and superscripts very difficult to read. The alignment of text with respect to the scanned images is poor, making the formulas even more difficult to follow.
All illustrations are also low-resolution scans.
The OCR mistakes are not confined to mathematical formulas. For example, there are mistakes such as replacing the word "component's" with "component-s".
In general the whole volume gives the impression of very sloppy work. Some figures have not been correctly cropped, and there are fragments of surrounding text still visible. The structure of the text (indentations, bullets, etc.) is lost in many places. The use of colours is distracting. Even the hyperlinks are wrong in some places - a sample input in an exercise has been turned into a list of hyperlinks into text chapters. The last page claims that there is also a web version, but the activation system does not seem to recognise this book.
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The original printed book is very well typeset, and it is really sad to see what the publisher has done when they prepared the Kindle edition.
I would have expected that the electronic sources of the original book would have been available; the book is not that old. Nevertheless, it seems that the publisher has just scanned a printed copy of the book, ran it through OCR, and done some rudimentary clean-up. In particular, it seems that nobody has actually bothered to proof-read the end result carefully.
Yet they are selling the Kindle version for > 100 USD...
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