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Technical Considerations for Cloud Application

Cloud technologies have lowered operational and capital expenditures, improved time to market (TTM), and provided organizations with an ability to dynamically adjust provisioning to meet changing needs globally. To decide workload placement, your highest priority should be understanding your organization’s business needs and pain points.

Select the technology that supports your goal. While some applications are better suited for public cloud, some are tailor-made for a private one. The four most important technical characteristics that help determine cloud workload placement are:


The primary use cases which apply to workloads with very high-performance scores include the following:

  • Performance and latency in relation to an end user’s location. For e.g., engineering solutions that reside physically near engineering departments.
  • Performance for resource-intensive transactions (compute, memory, and I/O) with guaranteed quality of service and response agreements.


Some applications process and house data such as personally identifiable information (PII), intellectual property (IP), and personal health information (PHI). These could cause harm to the organization if affected by malicious or accidental actions. What also needs to be considered is that whether or not security solutions are broadly available for a particular workload—security solutions are fairly mature for email workloads, for example.


The factors that present challenges to both traditional and cloud migrations are connections to other databases, frameworks, applications, workflows, and endpoints. The workload placement is impacted by the complexity and quantity of integrations because of the increased cost to integrate into multiple clouds. All the integrations should be assessed, modified, and refactored to meet the operational level agreement (OLA).

Data volume:

Data size and location (where the data is created and managed) are the two major factors. It can be challenging to transfer large datasets across distances.

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