ReadWriteWeb Solution Series

Things, as we know, fail. No matter how well you've set up the hardware or the software, something's going to break in your infrastructure. That's what you get for living in an entropic universe.

The best thing you can do is be as ready as you can for when that universe throws you a curve ball. In IT, that means a proper disaster recovery and business continuity plan should be in place.

Whether you are operating a virtual datacenter or a full-blown private cloud, you will need to ensure the security of your VMware systems just as you would your physical servers.

Virtual server security has the same rules of the road as any other type of IT security: protect the machine, reduce access, patch often: there's nothing new here. But virtual machines can also be maliciously (or even accidentally) configured to bring their own performance down or kill the whole machine itself.

Virtualizing your business critical apps is not a task to be done piecemeal. It can lead to confusion about job responsibilities, poor application performance and in the end a lot of money spent on new servers to make up for the taxed virtualized environment.

That may seem simple enough but the journey to virtualization is one with a history that dates back to a time in the IT world when complexity became a symptom of application proliferation.

How an enterprise approaches virtualization differs in the way it has traditionally deployed business critical applications.

The expectations for deploying Tier 1 applications prepared the enterprise for a lengthly endeavor. It was never done piecemeal. It involved the entire organization. Why? The applications changed the roles that people played. Business processes were affected. It had to be viewed holistically.

The explosion of data driven by sensors, data mining and social media and other Web-based interactions means that more and more companies will need to find ways of dealing with massive data sets - even companies that haven't typically been data driven before. But new business analytics applications may require more processing power than your organization has ever needed before, requiring you to find ways to handle data as efficiently as possible. Infrastructure-as-a-service providers and inexpensive data warehousing appliances with in-memory analytics will provide options for many organizations. But some may find distributed computing a better fit for their organization's big data needs.

Scientists and academics have been taking advantage of distributed computing for years, but it's an approach that can benefit information workers in other areas. Here are some methods of running applications in distributed environments, including some newer approaches.

One of the fun things about being a leader in IT is the opportunity to see new technology and explain it to others for the first time. We love to see people's eyes get big and excited when some new wizardry is introduced and we're the first one to explain it.

In the simplest terms, a key outcome of virtualization that the virtual engine software layer divides either hardware or software into more pieces than originally existed. Instead of one operating system running, the same hardware can run several concurrently. For example, these can even be of different flavors such as Linux and Windows running on the same Intel hardware. Where it gets interesting is that each additional system running doesn't divide computing power in half, like you might expect. Instead, an overall gain in system utilization is found in this approach. This "unused" power frees computing resources without the need to procure new hardware.