Big Data. Big Decisions
InformationWeek
Special Coverage Series

Commentary

George Crump

Storage That Only Looks Like A SAN

It isn't a perfect approach, but you can skip the SAN and still bring the advantages of local storage to a virtualized environment.

In my last column, I discussed how some vendors are abandoning shared storage for virtualized environments in favor of local storage. Their goal is to reduce cost and complexity while increasing performance.

Through the use of inter-host mirroring and replication they can still provide many of the key features of virtualization, but there are some problems: You need a complete second copy of a virtual machine (VM) on another host, you are limited to only that second host for failover or migration (unless you make multiple copies), and there is CPU consumption required of the second target VM. Essentially, you double your VM count and the resources those VMs require. In a resource-constrained environment, this could be a problem.

More Insights

Webcasts

More >>

White Papers

More >>

Reports

More >>

Vendors are trying to deliver other solutions that keep the cost, simplicity, and performance advantages of local storage solutions but that still provide VM flexibility and efficiency. One approach is the SAN-Less SAN.

[ For more on shared vs. local storage, see Is Shared Storage's Price Premium Worth It? ]

The SAN-Less SAN is actually another form of shared storage, but the storage is in the physical hosts of the virtual infrastructure instead of on a dedicated shared storage system. Each host is equipped with hard drives or Flash SSD storage, and as data is being stored it is written across each host in the infrastructure--similar to how data is written across the nodes of a scale-out storage cluster.

Redundancy is achieved by using a RAID-like data stripping technique so that failure of one host or the drive of one host does not crash the entire infrastructure. As in traditional RAID, the redundancy is provided without requiring a full second copy of data. Also, it is not uncommon for the disks in each node to themselves be RAIDed via a RAID card inside the server.

This technique of striping data across physical hosts provides the VM flexibility. All the hosts can get to the VM images, so a VM can be migrated in real time to any host.

One downside of the SAN-Less SAN approach is that you lose the performance advantage of pure local storage since parts of the data must be pulled from the other hosts. From a performance perspective, you have essentially created a SAN.

As discussed in my article, Building The SAN-Less Data Center, some vendors are merging features of local storage with this SAN-Less technique to bring the best of both worlds. These vendors are keeping a copy of each VM data local to the host on which it is installed in addition to replicating the VM’s data across the host nodes. The value of this technique is that the VM gets local performance until it needs to be migrated. A second step in migration allows the newly migrated VM to have its data rebuilt on its new host, restoring performance. This is especially intriguing if the local data is PCIe Solid State Disk.

Of course, nothing is perfect, and the network that interconnects these hosts must be well designed. There is also some host resource consumption as the software that runs the data replication on each host does its work. However, that consumption should not be as significant as a host loaded down with target VMs in the mirroring/replication example discussed in my last column. Finally, the type of hard disks and solid state disks used in the hosts in a SAN-Less SAN must also be carefully considered.

Despite the advantages of local storage and SAN-Less SANs, shared storage is far from dead. In my next column, I will look at local storage vs. SANs.

Even small IT shops can now afford thin provisioning, performance acceleration, replication, and other features to boost utilization and improve disaster recovery. Also in the new, all-digital Store More special issue of InformationWeek SMB: Don't be fooled by the Oracle's recent Xsigo buy. (Free registration required.)



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.