Big Data. Big Decisions
InformationWeek
Special Coverage Series

Commentary

Mark Peters

Virtualization And The Case For Universal Grid Architectures

Where We're Headed

(Page 2 of 2)

In short, we will eventually live in a world where physical boxes represent nothing other than containers that carry valuable resources, with all the resources in a data center (and even conceivably beyond) pooled, merged, and utilized for as long as required, and then relinquished back to the pool from which they came.

We'll have a pool of processing capabilities, memory, caches, and I/O from which VM and application requirements will carve out their requirements for the job at hand, and then those will disappear back into the pool until they are needed again.

More Insights

Webcasts

More >>

White Papers

More >>

Reports

More >>

This is not at all as far-fetched as it might seem. While not yet entirely automated, we have examples where this already works. High performance computing (HPC) environments have existed for years doing exactly this. In HPC, a single job or application is massively parallelized to execute small pieces across thousands of individual physical servers, performing a task thousands of times faster than if executing serially on a single processor. To the application, it's one machine: one really big machine with a ton of cores.

So what this tells us is that, if you want to span an application executable across physical nodes to process, you don't use a cluster. You use a grid.

But then, guess what the bottleneck is most of the time in HPC environments? I/O. Because, while the compute side may be grid, the storage side normally is a big, fast, fat, shared, monolithic storage instance. So, guess what has to change? Storage is the final frontier. We adopted storage clustering soon after server clustering and never really looked back. Today it seems as if 99% of all networked storage arrays are monolithic, two-controller (that is, clustered) boxes. When you run out of stuff in one box, you bring in another; maybe even cluster those together.

Yes, there are storage arrays that can support more than two-controller clusters today, but few. And even those tend to just be larger clusters--four pairs of two-controller clusters, for example. They are--essentially--still monolithic.

On the other hand, a grid is a federation of resources, unconstrained by traditional architectures. In the grid computing/HPC example, 1,000 servers with 1,000 network connections being squeezed down through two (or eight or 16 or 32) storage controllers only to then connect to 1,000 disk drives makes no sense. Why aren't there 1,000-disk controllers--virtual or otherwise? Eventually there will be. Just as users are restricted to their weakest physical link in virtual environments today, so it will be tomorrow ... unless something different is done.

Today's Computing Architectures

This diagram is what perhaps 95% of the commercial computing world looks like. Sure there are way more RAID controllers and switches, but the unit of measurement is pretty accurate: there's a lot on the top, a lot on the bottom, and not much in the middle.

It is important to realize that we are really in the first inning of the game. Sure, it's a new, big game, but look what's happened to our lives since 1972. IBM owned commercial computing, but that didn't stop the industry from constantly re-inventing itself and creating outrageous opportunity and wealth along the way. If VMware is the equivalent of IBM in 1972, which vendors will become the next EMC, Oracle, NetApp, etc.? (And yes, that's like saying who will be the next Digital, Wang, or Prime.)

We've pretty much been doing variations on the same architectural theme (by continuing to develop monolithic implementations of infrastructure) for over 50 years, and historically no significant trend lasts much longer. The time is ripe for an upheaval.

« Previous Page | 1 2  


Related Reading




Currently we allow the following HTML tags in comments:

Single tags

These tags can be used alone and don't need an ending tag.

<br> Defines a single line break

<hr> Defines a horizontal line

Matching tags

These require an ending tag - e.g. <i>italic text</i>

<a> Defines an anchor

<b> Defines bold text

<big> Defines big text

<blockquote> Defines a long quotation

<caption> Defines a table caption

<cite> Defines a citation

<code> Defines computer code text

<em> Defines emphasized text

<fieldset> Defines a border around elements in a form

<h1> This is heading 1

<h2> This is heading 2

<h3> This is heading 3

<h4> This is heading 4

<h5> This is heading 5

<h6> This is heading 6

<i> Defines italic text

<p> Defines a paragraph

<pre> Defines preformatted text

<q> Defines a short quotation

<samp> Defines sample computer code text

<small> Defines small text

<span> Defines a section in a document

<s> Defines strikethrough text

<strike> Defines strikethrough text

<strong> Defines strong text

<sub> Defines subscripted text

<sup> Defines superscripted text

<u> Defines underlined text

BYTE encourages readers to engage in spirited, healthy debate, including taking us to task. However, BYTE moderates all comments posted to our site, and reserves the right to modify or remove any content that it determines to be derogatory, offensive, inflammatory, vulgar, irrelevant/off-topic, racist or obvious marketing/SPAM. BYTE further reserves the right to disable the profile of any commenter participating in said activities.

Disqus Tips To upload an avatar photo, first complete your Disqus profile. | View the list of supported HTML tags you can use to style comments. | Please read our commenting policy.

Follow InformationWeek

By The Numbers

What Are Your Primary Concerns About Using Big Data Software?

Base: 417 respondents at organizations using or planning to deploy data analytics, BI or statistical analysis software
Data: InformationWeek 2013 Analytics, Business Intelligence and Information Management Survey of 541 business technology professionals, October 2012

What Do You Think?

What's your attitude about SQL analysis on top of Hadoop?
We want fast, standard SQL analysis capabilities on Hadoop ASAP
Hadoop is for unstructured data; SQL is for relational databases
We'll give SQL on Hadoop a try, but relational DBs will remain the mainstay
Given strong SQL support on Hadoop, we'd nix the data warehouse
We're not interested in Hadoop
No opinion



Related Content

From Our Sponsor

Five Big Data Challenges and How to Overcome Them with Visual Analytics

Five Big Data Challenges and How to Overcome Them with Visual Analytics

Business leaders often need a visual snapshot of data to quickly grasp and use it. This paper identifies five challenges in presenting data and how visual analytics can resolve them. Solutions are suggested to overcome the challenges of: speed, data clarity, data quality, displaying meaningful results, and dealing with outliers.

Game-Changing Analytics: How IT Executives Can Use Analytics to Create Innovation and Business Success

Game-Changing Analytics: How IT Executives Can Use Analytics to Create Innovation and Business Success

Today's competitive advantage requires a deeper understanding of your business, your market and your customers. As an IT executive, you can drive that knowledge transformation. In this white paper, learn how to make decisions as a strategic business leader and three steps to begin an analytics initiative within your enterprise.

Data Visualization Techniques: From Basics to Big Data with SAS Visual Analytics

Data Visualization Techniques: From Basics to Big Data with SAS Visual Analytics

High-performance data visualization turns sophisticated analyses into meaningful graphics, leading to faster and smarter decision making. In this white paper, learn how visual analytics can transform big data, with additional features such as real-time functionality, mobile compatibility, robust applications for technical groups and accessibility for nontechnical users.

Big Data: Lessons from the Leaders

Big Data: Lessons from the Leaders

Financial performance, competitive advantage, operational efficiency, strategic decision making - every business goal can extract value from big data, and the time for doubt or inaction has long passed. In this Economist Intelligence Unit report, in-depth interviews with data pioneers reveal the link between the effective use of big data and the bottom line among other results.

Decision-Driven Data Management: A Strategy for Better Decisions with Better Data

Decision-Driven Data Management: A Strategy for Better Decisions with Better Data

Which came first, the data or the decision? This white paper makes the case for having a decision in mind, then tailoring big data's volume, variety and velocity to achieve business results such as overcoming customer dissatisfaction or creating well-informed strategies in real time.

Informationweek Reports

Research: The Big Data Management Challenge

Research: The Big Data Management Challenge

The challenge of big data is real, but most organizations don't differentiate 'big data' from traditional data, and nearly 90% of respondents to our survey use conventional databases as the primary means of handling data. We'll help you understand what constitutes big data (it's not just size) and the numerous management challenges it poses.