COST 9: How do you manage demand, and supply resources?
For a workload that has balanced spend and performance, ensure that everything you pay for is used and avoid significantly underutilizing
instances. A skewed utilization metric in either direction has an adverse impact on
your organization, in either operational costs (degraded performance due to over-utilization), or wasted AWS expenditures (due to over-provisioning).
Resources
Getting started with Amazon SQS
AWS Auto Scaling
AWS Instance Scheduler
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Perform an analysis on the workload demand: Analyze the demand of the workload over time. Ensure the analysis covers seasonal trends and accurately
represents operating conditions over the full workload lifetime. Analysis effort should reflect potential benefit: for example, time spent
is proportional to the workload cost.
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Implement a buffer or throttle to manage demand: Buffering and throttling modify the demand on your workload, smoothing out any peaks. Implement throttling when your clients perform retries.
Implement buffering to store the request and defer processing until a later time.
Ensure your throttles and buffers are designed so clients receive a
response in the required time.
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Supply resources dynamically: Resources are provisioned in a planned manner. This can be demand-
based, such as through automatic scaling, or time-based, where demand is predictable
and resources are provided based on time. These methods result in the
least amount of over or under provisioning.
Improvement Plan
Perform an analysis on the workload demand
Analyze existing workload data : Analyze data from the existing workload, previous versions of the workload, or predicted usage patterns. Use log files and monitoring data to gain insight on
how customers use the workload. Typical metrics are the actual demand, in requests per second, the times when the
rate of demand changes or when it is at different levels, and
the rate of change of demand. Ensure you analyze a full cycle of the workload, ensuring you collect data for any seasonal changes such as
end of month or end of year events. The effort reflected in the analysis should reflect the workload characteristics. The largest effort should be placed on high value workloads that have the largest changes in demand. The least effort should
be placed on low value workloads that have minimal changes in demand. Common metrics for value
are risk, brand awareness, revenue or workload cost.
Forecast outside influence : Meet with team members from across the organization that can
influence or change the demand in the workload. Common teams would be sales, marketing or business development.
Work with them to know the cycles they operate with, and if there are any events that would change the demand of the workload. Forecast the workload demand with this data.
Implement a buffer or throttle to manage demand
Analyze the client requirements : Analyze the client requests to determine if they are capable
of performing retries. For clients that cannot perform retries, buffers will need
to be implemented. Analyze the overall demand, rate of change, and required response
time to determine the size of throttle or buffer required.
Implement a buffer or throttle : Implement a buffer or throttle in the workload. A queue such as SQS can provide a buffer to your workload components. Amazon API Gateway can provide throttling for your workload components.
Amazon Simple Queue Service
Amazon API Gateway
Supply resources dynamically
Configure time-based scheduling : For predictable changes in demand, time-based scaling can provide the correct amount
of resources in a timely manner. It is also useful if resource
creation and configuration is not fast enough to respond to changes in demand. Using
the workload analysis configure scheduled scaling using AWS Auto Scaling.
Scheduled Scaling for Amazon EC2 Auto Scaling
Configure Auto Scaling : To configure scaling based on active workload metrics, use Amazon Auto Scaling. Use the analysis and configure auto scaling to
trigger on the correct resource levels, and ensure that the workload scales in the required time.
Getting Started with Amazon EC2 Auto Scaling
