What infrastructure mapping configurations and pricing plans are assessed by HealthCheck for Azure?
The HealthCheck for Azure on-premises agent analyzes a customers' virtualized and/or physical environment over a 14-day period and then provides an assessment of the infrastructure as an Azure environment in the following mapping configurations and pricing plans.
|Hardware||This is a like-to-like mapping of the physical system configurations to an equivalent Azure instance and storage size. This mapping is based on system hardware specifications (e.g., number of CPUs, CPU speed, and assigned memory, disk size, etc.). This does not take actual workload or usage into account. The total cost of ownership (TCO) is estimated based on this configuration.|
|Workload||This mapping takes physical system configurations into account and incorporates actual workload and usage characteristics. That data is then projected to an Azure environment. Mapping of instance sizes, storage, and network demand is provided and the TCO is estimated based on the suggested configuration.|
Some of the workload characteristics considered when constructing your optimal plan include: peak CPU usage, disk occupancy, peak disk usage, peak network usage, unused compute or storage resources, disk IOPS and usage patterns. Since the current systems can be over- or under-provisioned, the TCO derived from workload-based mapping can be more or less than the TCO derived from the hardware-based mapping.
|Pay-as-you-go||HealthCheck for Azure provides a pay-as-you-go pricing assessment that utilize Azure's hourly rates and requires no up-front spending. Using the information gathered during 14-day on-premises agent analyzes, HealthCheck for Azure determines the optimized instance sizes that match the workload and usage requirements.|
|12-month prepay||HealthCheck for Azure also provides a prepaid pricing assessment that may result in lower hourly cost in exchange for up-front charges. Prepay plans can be made with a 12-month term. Optimal pricing plans takes usage hours of each machine and computes the recommendation that is most cost-efficient.|