Big Data. Big Decisions
InformationWeek
Special Coverage Series


SimpliVity's OmniCube Ties Data To VM, Not Hardware

Startup's new assimilated system packs server, networking, storage, and other resources in one appliance.

Microsoft Office 2013: 10 Best Features
Microsoft Office 2013: 10 Best Features
(click image for larger view and for slideshow)
Startup SimpliVity came out of stealth mode Monday with the announcement of OmniCube, an all-in-one virtual machine system for mid-size businesses. The box assimilates server, networking, and storage arrays with snapshot, deduplication, server virtualization, replication, and compression technologies.

Each OmniCube is a 2U--1.75-inch high--rack-mountable system that contains both standard server and storage resources, in addition to the SimpliVity underlying customized hardware and software that power the system. OmniCube ships with two six-core 2.5GHz Intel Sandy Bridge Xeon processors, four 200GB solid state drives, and 24TB of hard disk drives. The underlying technology, called OmniStack, combines the OmniCube software with a specialized PCI-e accelerator card responsible for handling processing-intensive algorithms, hardware acceleration, and index-related functionality.

Two or more OmniCubes are linked with each other via 10GbE to create a federation of nodes and a shared pool of resources that enables much of the end-user functionality of the system. Storage is directly attached to servers via internal SAS connections.

OmniCubes are deployed in sets of two or more systems for high availability and writes are mirrored simultaneously to both assimilated appliances. Pairs of OmniCubes can be clustered or federated locally, remotely, and across geographically distributed locations, as well as in the cloud for disaster recovery and business continuity.

[ Read HP Tools Simplify VM, Private Cloud Chores. ]

Unlike other systems, OmniCube data is not tied to the storage hardware, but to the virtual machine itself. Each application or VM knows which data it needs in order to do its job. Although each OmniCube knows only what data it contains, it keeps a real-time index of all the data sets in the federation. When an application or VM needs data, it polls other OmniCubes for it and receives data from the closest node in the federation.

As data is ingested by an OmniCube, it is deduplicated and compressed at inception, and maintained in this state throughout its lifecycle. This global deduplication and compression yields not only capacity savings, but also improves the granularity and efficiency of caching and optimizes data transport across the WAN.

SimpliVity’s OmniCube competes with converged systems from VCE, HP, IBM and Dell. Unlike those systems, which are cobbled together with existing server and storage resources, the OmniCube is built from the ground up to optimize data center operations.

SimpliVity was founded in 2009 by Doron Kempel, formerly CEO of Diligent, which was acquired by IBM. The company is funded by Accel Partners and Charles River Ventures for $18 million.

The system is expected to be available in November.

New innovative products may be a better fit for today's enterprise storage than monolithic systems. Also in the new, all-digital Storage Innovation issue of InformationWeek: Compliance in the cloud era. (Free with registration.)



Related Reading


More Insights




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.