Benefits of Azure Cloud Services for Cost Management

C

Elasticity in this case is the ability to provide additional compute resource when needed and reduce the compute resource when not needed to reduce costs.

Autoscaling is an example of elasticity.

Elastic computing is the ability to quickly expand or decrease computer processing, memory and storage resources to meet changing demands without worrying about capacity planning and engineering for peak usage. Typically controlled by system monitoring tools, elastic computing matches the amount of resources allocated to the amount of resources actually needed without disrupting operations. With cloud elasticity, a company avoids paying for unused capacity or idle resources and doesn't have to worry about investing in the purchase or maintenance of additional resources and equipment.

The correct answer to this question is C. Elasticity.

Elasticity is a benefit of Azure Cloud Services that supports cost management for applications that experience fluctuations in usage patterns. Elasticity allows the application to dynamically scale up or down its computing resources (such as CPU, memory, and storage) based on the current demand.

In the case of App1, the application experiences low usage during the first three weeks of each month and very high usage during the last week of each month. With elasticity, the application can scale up its computing resources during the last week of each month to handle the increased demand and scale them back down during the first three weeks when the demand is low. This way, the company can avoid paying for unused resources during low-demand periods and only pay for the resources they need during peak periods.

High availability, high latency, and load balancing are also benefits of Azure Cloud Services, but they are not directly related to cost management for fluctuating usage patterns.

High availability ensures that an application is always available and accessible to users, even in the event of hardware or software failures.

High latency refers to the delay in data transmission between the application and the user. Azure Cloud Services can minimize latency by placing the application closer to the user or by using a content delivery network (CDN) to cache content closer to the user.

Load balancing distributes incoming network traffic across multiple servers to ensure that no single server is overloaded. This can improve application performance and availability.

In summary, elasticity is the benefit of Azure Cloud Services that supports cost management for applications with fluctuating usage patterns. By dynamically scaling up or down computing resources based on demand, the company can avoid paying for unused resources and only pay for the resources they need during peak periods.

The solution of recommending the use of the scalable expenditure model to migrate all virtual machines to an Azure pay-as-you-go subscription can meet the goal, depending on the specific requirements and characteristics of the workload.

The scalable expenditure model is one of the pricing models available in Azure that allows customers to adjust their resources dynamically based on demand, which can result in cost savings compared to fixed pricing models. With the scalable model, customers pay only for the resources they consume, and can scale up or down resources as needed.

However, whether this solution meets the goal depends on factors such as the workload's resource utilization patterns, performance requirements, and availability needs. If the workload has variable resource requirements and can tolerate occasional performance fluctuations, the scalable expenditure model can be an appropriate choice. However, if the workload requires a fixed amount of resources to run optimally, the fixed pricing model may be more appropriate, as it provides predictable costs and performance.

Additionally, the scalable expenditure model may not be the best choice if the workload requires high availability or strict compliance requirements, as scaling down resources during periods of low demand may affect availability or compliance. In these cases, a fixed pricing model with reserved instances or dedicated hardware may be more appropriate.

Therefore, to determine whether the solution of recommending the scalable expenditure model meets the goal of using the correct expenditure model for migrating virtual machines to Azure, a thorough analysis of the workload's characteristics, requirements, and usage patterns is necessary.