MIDACOST05-BP01 Implement a buffering or throttling approach
Implement balanced resource utilization that handles varying workload demands while maintaining cost efficiency for manufacturing systems. This includes prioritizing critical processes while queuing less time-sensitive tasks and implementing appropriate scaling triggers aligned with production cycles.
Desired outcome: Balanced resource utilization that handles varying workload demands while maintaining cost efficiency.
Common anti-patterns:
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Implementing throttling on time-critical manufacturing processes
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Using the same buffering strategy for all types of industrial data
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Overlooking real-time requirements of production monitoring systems
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Setting queue limits without considering production batch sizes
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Implementing aggressive throttling that impacts quality data collection
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Not accounting for upstream and downstream dependencies in manufacturing processes
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Using standard IT buffering patterns without adapting to manufacturing needs
Benefits of establishing this best practice:
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Controlled resource consumption
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Avoided system overload
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Optimized costs during peak periods
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Improved system stability
Level of risk exposed if this best practice is not established: Medium
Implementation guidance
Before you begin, you will need:
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Documented critical and non-critical manufacturing processes
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Peak resource utilization patterns for different production phases
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Response time requirements for various manufacturing systems
Key decisions needed:
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Resource allocation priorities for critical vs. non-critical processes
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Throttling thresholds for different types of manufacturing workloads
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Queue configurations for deferrable processes
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Scaling triggers aligned with production cycles and peaks
Implement buffering and throttling mechanisms to manage cloud resource utilization during manufacturing peaks. Design a system that prioritizes critical processes (for example, real-time monitoring, quality control) for immediate resource access, while queuing less time-sensitive tasks (for example, batch analytics, report generation). Use auto-scaling for baseline capacity but implement throttling to help prevent non-critical tasks from consuming resources needed for production-critical operations.
Consider the following:
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Using Spot Instances for interruptible, non-critical workloads
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Implementing reserved capacity for predictable, critical processes
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Using serverless technologies for sporadic, scalable tasks
Regularly review and adjust your buffering and throttling strategies based on changing production patterns and business needs.
Implementation steps
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Identify and categorize manufacturing workloads:
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Critical real-time processes (for example, process control, safety systems)
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Time-sensitive operations (for example, quality inspections, inventory updates)
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Deferrable tasks (for example, long-term analytics, reporting)
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Design resource allocation strategies:
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Priority-based access for critical systems
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Queueing mechanisms for non-critical operations
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Load balancing across production lines or facilities
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Implement OT-aware monitoring:
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Set up real-time monitoring for critical production KPIs
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Configure alerts based on manufacturing thresholds
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Integrate with SCADA or MES for comprehensive visibility
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Establish OT-IT integrated scaling mechanisms:
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Automatic scaling triggered by production volumes
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Resource reservation for planned production increases
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Gradual scale-down aligned with shift changes or maintenance windows
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Conduct regular performance and cost reviews:
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Analyze resource utilization against production output
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Identify opportunities for optimization without impacting OT
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Adjust strategies based on changing manufacturing requirements
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Implement feedback loops with shop floor:
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Gather input from operators on system performance
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Align IT resource adjustments with production schedules
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Continuously refine based on real-world manufacturing impact
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Key AWS services
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Amazon SQS
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Amazon Kinesis
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AWS Auto Scaling
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Amazon API Gateway
Resources
Related documents:
