IT managers have recently been forced by rapidly changing market dynamics and regulations to find ways to lower their static data center's computing costs and resource consumption. Transforming an industry-standard, system-based, static data center into a virtualized, automated, dynamic data center can lower costs and increase efficiency. Through virtualization and automation, Knowledge Center contributor Daniel Kusnetzky explains how you can transform your static data center into a dynamic data center.
centers, especially those based upon industry-standard systems and
software, have historically been static environments. That is to say,
one consisting of a server configured to support a single operating
system, data management system, application framework and a number of
applications. Systems then access both storage and the network using a
pre-assigned configuration that can only be changed with a carefully
planned set of manual procedures.
As the users of mainframes and single-vendor midrange systems discovered nearly three decades ago
this type of static thinking leads to a number of problems and must be
replaced by the careful use of virtualization and automation. Although
adopting dynamic, adaptive thinking is an important step, it is still
important to remember that physical machines (including systems,
network and storage), must be running for all of this to work. This is
a lesson the managers of industry-standard, system-based data centers
are just learning now.
How to avoid overprovisioning
Today's industry-standard, system-based data centers often evolved
without an overarching plan. So, each business unit or department
selected systems and software to satisfy only its own requirements.
Selections were made to support only that business unit or department's
own flow of business. This means that most data centers have become a
warehouse--something a few would call a museum--for "silos of
computing." Each silo of computing was purchased with an eye only to
each individual business unit or department's needs. Each silo was
often managed with its own management tools (that may not play well
with other tools the organization is relying on for the management of
Business units and departments purchased sufficient system,
software, storage and network resources to handle their own peak
periods. Sufficient resources were also purchased to provide enough
redundancy so that business solutions were always up and available.
This approach also had an expensive side effect: those resources
ended up sitting idle, waiting for peak periods a great deal of the
time. If all of the organization's idle resources are considered, a
great deal of the organization's IT investment has been wasted. After
all, they are not available for the day-to-day processing requirements
of the organization. It is clear that this approach--an approach that
seemed reasonable and prudent only a few years ago--is now a luxury
that many organizations can no longer afford. Organizations have been
forced by a global market (and rapidly changing market dynamics and
regulations) to include efficiency and making best use of their
resources in their list of priorities.
How to overcome problematic manual processes
When outages occur in the static data center, many organizations
turn to error-prone manual processes and procedures to determine what's
happening. They will isolate the problem, move resources around so that
the business can keep running, fix the problem, and then move resources
back to their normal configuration. It's also necessary to get physical
machines turned on and loaded with the appropriate software. The
network must be restarted or reconfigured. Storage systems must be
restarted and reconfigured. Speed of recovery is heavily dependent upon
getting the physical systems back up and configured.
Each of these steps can take a great deal of time, require costly
expertise that the organization doesn't normally have on staff, and is
subject to human error. Another complication is that each of the
computing silos is based upon different application and management
frameworks. This means that staff expertise which works well in solving
one part of the problem is not the same staff expertise needed to solve
other parts of the problem.
It is clear that manual processes don't scale well. This, of course,
is the reason mainframe and midrange-based data centers turned to
automation decades ago. Organizations want to work with a dynamic data
center that has the ability to deal with planned and unplanned
outages--to roll back the clock--without dealing with any of the
painful issues mentioned earlier.