So, moving those 45 percent of instances to the cloud would net $5.6M in annual savings — a 17 percent reduction, without any need to change applications.
But it’s not always economically advantageous to head for the cloud. When the cost of re-platforming legacy applications exceeds the savings, it may not make short-term sense, but would, we believe, come out positive over the course of five years. Accordingly, we estimate the pace of cloud migration for the enterprise will continue to accelerate incrementally, with between 10 percent and 20 percent of enterprise workloads moving to the cloud in 2017.
Still, cloud does not have to be an all-or-nothing proposition, and based on the financial, compliance, and security needs of your applications, instances can operate both on premise and in the cloud. And server refresh is also an option for optimizing costs.
But the bottom line — and the wave of the future — indicates that cloud is a great fit for a large and growing percentage of currently deployed compute and storage infrastructure. The key question then becomes: which instances should you move and how do you find them to bring home the savings?
The answer to these questions can be found by exploring the provisioning, usage, and utilization patterns of your current compute, and then building an unbiased economic model around it. The challenge is churning through hundreds-of-millions of data points to find the best fit for your workloads, while ensuring the results are not tainted by the hand that holds the pencil, so to speak.
To that end, many of today’s leading enterprises, cloud providers and consultants use analytical and algorithmic decision making platforms to assist with the rightsizing and right-costing of enterprise compute. Adopting the right analytical solution can enable your organization to identify the best fit — on premise or cloud, instance size, and pricing option — to accommodate the real-time compute needs of your enterprise workloads.
While analytic and algorithmic analysis solutions will come in various shapes and sizes, to find the right fit there are a few key things to consider. The solution should be one that is cost-effective and does not require new agents to be deployed. Algorithms that have been validated by both hardware and cloud providers will ensure the outcomes are vendor agnostic and always represent your ideal future states. Lastly, the solution should contain a reference library of current on-premise costs, which will allow you to get immediate value and make it easier to identify the best fits for cloud.
By looking at the real-time compute needs of your current workloads and associating it with the economics of ideal future states, you will have the tools to make “To Cloud or Not to Cloud?” an easier question to answer.
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