# MIDASUS02-BP01 Actively manage workloads and resource allocation based on production demands
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 Identify critical and non-critical manufacturing systems, then align computing resources with actual production schedules and operational requirements to reduce waste while providing reliability for the essential manufacturing systems. 

 **Desired outcome:** Cloud resources that efficiently scale with manufacturing operations, prioritizing time-sensitive shop floor systems while optimizing resource usage for enterprise applications, resulting in reduced energy consumption, lower costs, and improved environmental sustainability. 

 **Benefits of establishing this best practice:** 
+  Reduced energy consumption and carbon footprint by removing over provisioning of resources. 
+  Lower operational costs while maintaining reliability for the production systems. 
+  Enhanced sustainability reporting metrics with quantifiable improvements in cloud resource efficiency aligned with manufacturing operations. 

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

## Implementation guidance
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+  Analyze production schedules and system criticality to categorize manufacturing applications as time-critical (shop floor control, real-time monitoring) and non-time-critical (reporting functions, data processing) using workload assessment tools. 
+  Implement auto scaling mechanisms for all manufacturing systems based on their specific demand patterns, verifying that enterprise planning and design systems maintain necessary availability while optimizing resource allocation. 
+  Use batch processing systems for scheduling background data processing jobs like quality analysis, production reporting, and maintenance analytics during periods of lower resource demand. 
+  Deploy high performance computing (HPC) solutions and run resource-intensive engineering workloads such as computational fluid dynamics (CFD) and computer aided engineering (CAE) simulations during off-peak production hours to optimize resource utilization. 

### Implementation steps
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1.  **Assessment and classification:** 
   +  Conduct workload assessment of manufacturing applications using AWS Well-Architected Tool 
   +  Document peak usage patterns using Amazon CloudWatch 
   +  Classify applications into real time and batch processing categories 

1.  **Demand pattern mapping:** 
   +  Create demand heat maps using Amazon CloudWatch metrics 
   +  Identify off-peak windows for non-time-critical workloads 

1.  **Resource optimization configuration:** 
   +  Configure AWS Auto Scaling policies with appropriate thresholds 
   +  Implement scaling plans that align with production schedules 
   +  Define resource constraints to help prevent over-provisioning 

1.  **Workload scheduling implementation:** 
   +  Create AWS Batch job configurations for non-critical processing tasks 
   +  Configure AWS ParallelCluster for engineering simulations during off-hours 
   +  Implement prioritization logic for computing resources 

1.  **Monitoring and continuous improvement:** 
   +  Deploy CloudWatch dashboards to track resource utilization efficiency 
   +  Establish KPIs using AWS Cost and Usage Reports
   +  Create sustainability improvement reporting and quarterly review process 

## Key AWS services
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+  AWS Auto Scaling 
+  Amazon EC2 
+  AWS Batch 
+  AWS ParallelCluster 
+  Amazon CloudWatch 
+  AWS Cost and Usage Reports 

## Resources
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+  [Step and simple scaling policies for Amazon EC2 Auto Scaling](https://docs.aws.amazon.com/autoscaling/ec2/userguide/as-scaling-simple-step.html) 
+  [AWS Batch](https://docs.aws.amazon.com/batch/latest/userguide/what-is-batch.html) 
+  [AWS ParallelCluster](https://docs.aws.amazon.com/parallelcluster/latest/ug/what-is-aws-parallelcluster.html) 
+  [Amazon EC2 instance types](https://aws.amazon.com/ec2/instance-types/)