Blade Servers and Virtualization: Transforming Enterprise Computing While Cutting Costs Review

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Blade Servers and Virtualization: Transforming Enterprise Computing While Cutting Costs ReviewIt doesn't take very long to figure out that the authors really like blade servers.
They like their space savings, cable plant simplicity, and uniformity. There are very detailed (although never critical) descriptions of each manufacturer's product lines and some very exhaustive matrices comparing not only blade servers but virtualization products as well.
My problems are in the details. First of all, it is clear that in many cases the benefits being credited to blade servers accrue ONLY because those blade servers are running virtualization products. Moreover, in many cases it is unclear why the benefits would not have accrued to commodity scale out servers running virtualization products.
Two, there are some claims that just stretch credulity too far. For instance, it is claimed that a provisioning a new blade server can be done in a few hours while doing the same with a rack mount server can take weeks.
Bearing in mind that Google, Yahoo, and Microsoft have all constructed their massive data centers with rack mount servers, you have to wonder how they could have made the same mistake (from the author's point of view) several hundred thousand times for each vendor.
The fact of the matter is that blades are a perfect choice for certain data center environments, but not others. Today they constitute ~ 15 % of all server shipments and most vendors hope to get that up to ~20% by 2010.
What's the hold up? Well, blade servers cost more per unit of computing power. Their form factors are proprietary, so their is considerable vendor lock-in (and thus higher prices). And the switches built for each blade center are vastly more expensive and slower to adopt new features than standalone modular or fixed form factor switches.
Conversely, virtualization is a wonderful add-on for certain types of compute applications, but not all. (For the technically inclined, if compute latency is at a premium, or the computations requires large amounts of state to be held in memory, then virtualization is going to be at best a mixed blessing and quite possibly make things worse.) Such limitations are alluded to peripherally, but never spoken to head on.
That's a darn shame. Had the authors included a chapter on when blades make sense and when virtualization makes sense, this would have easily been a 5 star book for me. The writing style is lucid and flows well. The authors are clearly experienced in their field. However, the assertion that blades and virtualization are a broad based panacea for most types of organizations and data centers just does not conform to the facts.Blade Servers and Virtualization: Transforming Enterprise Computing While Cutting Costs Overview
Blade server systems and virtualization are key building blocks for Next Generation Enterprise Data centers
Blades offer modular, pre-wired, ultra high-density servers (up to 10x traditional servers) with shared components (power, cooling, switches) – reducing complexity and cost, and improving flexibility, availability, manageability, and maintainability
Virtualization enables consolidation of physical servers by allowing many virtual servers to run concurrently on one physical server – improving system utilization, reducing the total number of physical servers, reducing costs, and increasing flexibility
This is the first book covering these complementary technologies and how, together, they provide a strong foundation for the future
It examines the history, architectures, features, examples, and user case studies of blade systems and virtualization, and offers guidance and considerations for how to evaluate and implement solutions


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Introduction to Information Retrieval Review

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Introduction to Information Retrieval ReviewI am a big fan of the authors 1999 book on Statistical Natural Language Processing, and I and was thrilled when I found this new book online -- just search for "Information Retrieval" on Google.
In these two books, they describe the theory behind a vast toolbox which can be used to construct new tools/products for the Internet. Now I can go back to them when the need arises.
For starters, I appreciate the detailed theoretical explanations of topics that I could not find in other texts, and the references to related work are especially helpful. One of the other books I read was Information Retrieval by Grossman, which is an older book but has a more condensed style compared to this. Grossman's discussion of clustering was more high level and referenced a few more papers that I found useful. That helped increase my interest to read through these chapters in which offer greater detail.
Before I felt like I could place each topic in its appropriate context, I had to spend six months of reading both the books, playing with code and finding s/w packages, searching the research literature, reading papers and other books, and then cycling back to the books. Here's are some suggestions for things I'd like to see:
1. A set of recomended programming tools: in some books on Perl -- such as the chapter "Natural Language Tools" in pages 149-171 in "Advanced Perl Programming" by Simon Cozens (O'Reilly) -- you get a very "quick & dirty" introduction to maybe 20-30% of the concepts in these two books along with ways to implement and play around with them. Although Perl has many natural language processing tools, the Cozens book cuts to the chase, explains which are the best tools, and shows you how to use them. I think knowing such shortcuts aids in learning how to apply and improve on them. The more complex and sophisticated topics, the more likely to make it out into the real world if they are easy to play with.
2. More data/examples on what does/doesn't work with end-users: Numbers, graphs, and charts are all good stuff. I always appreciate it when the authors referenced quantitative comparisons, real-world products, and history of Internet. One of the reasons I had to consult the research literature was to broaden my understanding of quantitative comparisons between different techniques involving end-users, which were typically done in the context of complete systems studies that users could try out.
Thanks,
-SriIntroduction to Information Retrieval OverviewClass-tested and coherent, this groundbreaking new textbook teaches web-era information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. Written from a computer science perspective by three leading experts in the field, it gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Although originally designed as the primary text for a graduate or advanced undergraduate course in information retrieval, the book will also create a buzz for researchers and professionals alike.

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