How might companies deal with hardware dependencies while moving toward a virtualization model?
Up until fairly recently, companies didn't really have a choice. Enterprise technologies were almost universally housed in on-premises server rooms, and were entirely hardware dependent. The technology industry didn't even really see a way away from this until the cloud stampede of the past two decades, where the principle of web-delivered services freed enterprise data from its prison.
At the same time, companies were approaching another new technology principle based on logical partitions – virtualization. The idea of virtualization is that instead of having hardware pieces linked together, companies utilize a central pool of CPU and memory, and allocate that to various virtual machines that play different roles within the network context.
All of this has taken place fairly quickly. Now companies are moving away from systems that are made to operate on “bare metal” or in any particular hardware environment. They're moving toward either the cloud, or virtualization, or both. These big steps allow them to save money on hardware procurement. They allow companies to do away with the responsibility of painstakingly maintaining servers in cool rooms, or trying to get in-house staff to integrate more hardware pieces with Ethernet cabling.
With that in mind, companies must move behind the old paradigms and get away from hardware dependencies in general.
First, they must make sure that new virtualized systems contain enough resources to simulate what happens in the hardware-dependent legacy system. Experts point out that virtualization does increase overall resource requirements by a small margin – so trying to just plunk a large, resource-hungry system into a new virtualized network may not work well.
Companies also need to migrate data away from legacy systems. In most cases, this involves simply porting the data to a new system, duplicating it, and decommissioning the old system which was so inherently constrained. However, in some tough cases, migration has to be done by hand with excruciating data entry. In these types of troublesome situations, companies have to figure out whether it's truly worthwhile to save the data, and if so, how it should be specifically transported into a new modern platform.
In general, companies need to learn to manage new models. They have to understand, for example, the security requirements for a cloud or virtualized system, and how security is different when the data doesn't reside in a particular bare metal environment. They have to understand how to analyze and evaluate virtualized networks, which are so inherently complex that they often require sophisticated dashboards for daily observation. For example, technicians have to understand the effects of undersized and/or oversized virtual machines, identify bottlenecks and understand workload management and performance optimization.
Through these types of objectives, companies can get closer to full confidence in new IT models and put away the burden of hardware-dependent data setups to enjoy more of the benefits of what 21st-century technology has to offer them.
More Q&As from our experts
- What are some of the dangers of using machine learning impulsively without a business plan?
- What is TensorFlow’s role in machine learning?
- Why are companies paying so much for AI professionals?
- Virtualization Health Chart
- Data Migration
- Cloud Computing
- Virtual Reality Modeling Language
- Virtual Appliance
- Desktop Virtualization
Tech moves fast! Stay ahead of the curve with Techopedia!
Join nearly 200,000 subscribers who receive actionable tech insights from Techopedia.