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LSSUS02-BP01 Implement sustainability proxy metrics pipeline for research workloads - Life Sciences Lens
Implementation guidanceResources

LSSUS02-BP01 Implement sustainability proxy metrics pipeline for research workloads

Establish comprehensive tagging strategies and normalized metrics to track and measure sustainability performance across research workloads. Implement proxy metrics that correlate resource consumption with research outputs to enable meaningful comparisons and optimization opportunities. Create sustainability KPIs that combine resource utilization data with research milestones to drive continuous improvement in energy efficiency and environmental impact.

Desired outcome: Establish measurable sustainability metrics that enable data-driven optimization of research workloads and provide clear visibility into environmental impact across different research activities and architectural patterns.

Common anti-patterns:

  • You don't implement consistent tagging strategies across research workloads and resources.

  • You measure absolute resource consumption without normalizing for research outputs.

  • You don't establish baseline metrics to track sustainability improvements over time.

  • You collect metrics but don't integrate them into research workflow planning and optimization.

  • You don't correlate resource consumption with actual research deliverables and milestones.

  • You rely solely on cost metrics without considering environmental impact measurements.

  • You don't establish regular review cycles to assess and act on sustainability metrics.

Benefits of establishing this best practice:

  • Enable data-driven decisions for optimizing research workload sustainability.

  • Provide clear visibility into environmental impact across different research activities.

  • Support regulatory adherence and sustainability reporting requirements.

  • Align research operations with organizational sustainability goals and commitments.

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

Implementation guidance

Sustainability metrics for research workloads require a strategic approach that balances measurement granularity with practical implementation. Life sciences organizations must track resource consumption in ways that correlate with research outputs, enabling meaningful comparisons across different types of analyses and computational approaches. This is particularly important given the diverse nature of life sciences computing, from high-throughput genomics processing to complex molecular modeling simulations.

Proxy metrics serve as practical indicators of sustainability performance when direct energy measurements are not feasible. By normalizing resource consumption against research outputs (such as vCPU hours per genome processed or GPU hours per molecular structure analyzed), organizations can identify optimization opportunities and track improvements over time. Integration with the AWS Customer Carbon Footprint Tool provides additional context by correlating proxy metrics with actual carbon footprint data.

Implementation steps

  1. Establish comprehensive tagging strategy for research workloads:

    • Implement consistent tags for research type (genomics, proteomics, drug discovery).

    • Tag resources by project phase (discovery, validation, production).

    • Include architectural pattern tags (batch processing, interactive analysis, real-time).

    • Use AWS Resource Groups and Tag Editor for centralized tag management.

  2. Define and implement normalized sustainability metrics:

    • Calculate vCPU hours per genome processed for genomics workloads.

    • Track GPU hours per molecular structure analyzed for computational chemistry.

    • Measure storage efficiency through data processed per TB stored.

    • Use Amazon CloudWatch custom metrics to track normalized performance indicators.

  3. Create sustainability KPIs aligned with research outputs:

    • Combine resource utilization metrics with research milestone achievements.

    • Track energy efficiency improvements over time using baseline comparisons.

    • Implement cost-per-research-output metrics for comprehensive sustainability assessment.

    • Use AWS Cost and Usage Reports for detailed resource consumption analysis.

  4. Integrate metrics with AWS Customer Carbon Footprint Tool:

    • Correlate proxy metrics with account and Region-level carbon footprint data.

    • Establish regular reporting cycles for sustainability performance.

    • Create dashboards that combine resource efficiency with environmental impact.

    • Use Quick for sustainability reporting and visualization.

  5. Establish continuous monitoring and optimization processes:

    • Integrate sustainability metrics into research workflow planning.

    • Conduct regular sustainability reviews with research teams.

    • Create feedback loops to incorporate sustainability learnings into future research planning.

Resources

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