Cloud can be puzzling for enterprises considering the various options of provisioning and configurations available out there. Assessing these options from Cloud provider(s) can be a complex and tiring business and at times leads to over-provisioning and sub-optimal configurations. CoreIT feels that employing scientific analysis can help to dramatically reduce the cost by around 80% with reduced application risk.

Here it is important to understand what scientific analysis could mean. Here is a short description of what that entails as two critical aspects of application workloads:

Workload analysis:
Workload analysis is one of the critical factors that help to determine the optimal host environment and resource requirement for an application. Analysis of the workload pattern can help to dictate the optimal hosting strategy for a business cycle and when a workload is likely to make more use of resources an on-off mechanism can be set by which the use of public cloud IaaS to regulated the cost for usage.

Instances where resources are required to run even at low periods of use can employ a containerized environment that supports resource overcommit as cost effective hosting option. If the same app is rewritten to scale horizontally, then the time-boxed approach can prove fruitful as a great model for hosting.

Fit-for-purpose analysis:
A detailed fit-for-purpose analysis helps to make informed placement decisions that are compliant with business rules and application requirements. Determining whether workload can be a good candidate for cloud is a daunting question. Since most applications have technical, security, privacy or regulatory policies answering such a question requires detailed analysis. Based on requirements of application components, an ideal cloud hosting strategy can be scientifically determined, and guesswork can be eliminated from the process.

CoreIT believes that scientific analysis to solve cloud placement issues and enabling to uncover a reliable choice with low cost and lowest risk

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