# MIDACOST05-BP02 Implement dynamic resource provisioning
<a name="midacost05-bp02"></a>

 Enable automated resource scaling that matches manufacturing workload demands while optimizing costs. This includes implementing warm pools for faster scaling, considering application warm-up times, and aligning scaling policies with production schedules and peak processing times. 

 **Desired outcome:** Automated resource scaling that matches manufacturing workload demands while optimizing costs. 

 **Common anti-patterns:** 
+  Implementing automatic scaling without considering production schedule requirements 
+  Setting scaling thresholds without consulting manufacturing operations teams 
+  Using the same scaling policies for both production and non-production workloads 
+  Neglecting warm-up times for manufacturing applications when scaling 
+  Implementing aggressive scale-in policies that could impact production monitoring 
+  Not accounting for data retention requirements when scaling storage resources 
+  Ignoring the impact of scaling on integrated manufacturing systems 
+  Setting up dynamic provisioning without consideration for compliance requirements 

 **Benefits of establishing this best practice:** 
+  Optimized resource utilization 
+  Reduced manual intervention 
+  Cost-efficient scaling 
+  Improved responsiveness 

 **Level of risk exposed if this best practice is not established:** Medium 

## Implementation guidance
<a name="implementation-guidance-66"></a>

 Before you begin, you will need: 
+  Detailed production schedules and patterns 
+  Peak resource usage data by workload type 
+  System warm-up and response time requirements 

 Key decisions needed: 
+  Scaling thresholds for different manufacturing workloads 
+  Resource retention periods based on production needs 
+  Performance impact limits for critical systems 
+  Cost optimization targets by workload type 

Design your manufacturing workloads to automatically adjust resource provisioning based on current demand and production schedules. Implement a data-driven approach that correlates IT resource needs with manufacturing operations, providing appropriate safeguards for critical production systems and consideration for startup times and warm pools. 

### Implementation steps
<a name="implementation-steps-46"></a>

1.  Define scaling metrics: 
   +  Production demand indicators 
   +  Resource utilization thresholds 
   +  Cost constraints 

1.  Configure auto scaling policies: 
   +  Scale-out conditions 
   +  Scale-in conditions 
   +  Cool-down periods 

1.  Implement monitoring. 

1.  Set up cost tracking. 

1.  Perform regular policy review and optimization. 

## Key AWS services
<a name="key-aws-services-28"></a>
+  AWS Auto Scaling 
+  Amazon EC2 Auto Scaling 
+  AWS Lambda 
+  Amazon CloudWatch 

## Resources
<a name="resources-67"></a>

 **Related documents:** 
+  [AWS Auto Scaling](https://docs.aws.amazon.com/autoscaling/plans/userguide/what-is-aws-auto-scaling.html) 
+  [Amazon EC2 Auto Scaling User Guide](https://docs.aws.amazon.com/autoscaling/ec2/userguide/what-is-amazon-ec2-auto-scaling.html) 
+  [AWS Lambda: Configuring reserved concurrency for a function](https://docs.aws.amazon.com/lambda/latest/dg/configuration-concurrency.html) 
+  [AWS Lambda: Configuring provisioned concurrency for a function PDF RSS](https://docs.aws.amazon.com/lambda/latest/dg/provisioned-concurrency.html) 
+  [Amazon CloudWatch: Using Amazon CloudWatch alarms](https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/AlarmThatSendsEmail.html) 
+  [Predictive scaling for Amazon EC2 Auto Scaling](https://docs.aws.amazon.com/autoscaling/ec2/userguide/ec2-auto-scaling-predictive-scaling.html)