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# Minimising the Carbon Footprint of Workloads

Idling servers still consume 60% of their peak power. Slash your infrastructure's carbon footprint using kernel-level energy profiling, strict right-sizing, and strategic workload time-shifting.

- **Speakers:** [Michael Mueller](https://www.wearedevelopers.com/@michael-mueller)
- **Event:** World Congress 2024
- **Published:** August 29, 2024
- **Duration:** 27:46
- **URL:** https://www.wearedevelopers.com/videos/1198-minimising-the-carbon-footprint-of-workloads

## Summary

IT workloads currently account for 4–5% of global carbon emissions, a figure projected to triple amid the explosive growth of AI training and inference. Despite looming regulatory crackdowns on data center power usage effectiveness (PUE), many organizations remain constrained by low visibility, greenwashing, or reporting tools that lack actionable engineering insights.

The most profound immediate impact comes from maximizing resource utilization. Because idling servers still consume roughly 60% of their peak power, driving system utilization into the 60–80% sweet spot ensures energy proportionality and maximum value per watt. Organizations can further tackle their footprint by aggressively decommissioning zombie servers, extending physical hardware lifespans to amortize embedded carbon, and leveraging strict right-sizing and auto-scaling rather than trusting default vendor configurations.

Teams can track progress using the Software Carbon Intensity (SCI) standard alongside profiling tools like Kepler or Scaphandre to measure energy required at the kernel level. Beyond right-sizing, engineers can practice time-shifting heavy batch jobs to hours when renewable energy is plentiful, or region-shifting workloads to grids with low carbon intensity, though hyped concepts like follow the sun should be avoided due to data center capacity impacts. At the software level, dedicated application profiling to prevent memory leaks and blocking calls typically yields higher sustainability returns than simply rewriting codebases in more efficient languages.

**Keywords:** sustainable IT infrastructure, green workloads, PUE compliance, SCI tracking, embedded carbon amortization, energy proportionality, carbon-aware region shifting, cloud emissions monitoring, kubernetes energy profiling, kepler power measurement, server utilization optimization, workload rightsizing, zombie server decommissioning, hardware lifespan extension, green coding practices, scaphandre linux profiling

## Chapters

1. **Rising technology impact on global overall carbon emissions** (00:01) — Global carbon dioxide emission goals directly intersect with the projected tripling of cloud computation output.
1. **Environmental cost of large artificial intelligence language models** (02:05) — Training and running automated inference for generative language models requires massive and unsustainable global power output.
1. **Navigating incoming infrastructure grid power and efficiency regulations** (04:56) — Emerging constraints on localized power grids alongside incoming energy efficiency laws completely alter future computing center builds.
1. **Carbon transparency reporting issues from centralized cloud providers** (06:29) — Late external reporting systems and a consistent lack of real-time analytics currently prevent application engineers from optimizing baseline carbon emissions.
1. **Optimizing active server utilization limits and power effectiveness** (08:06) — Pushing operational hardware utilization percentages closer to optimal thresholds balances performance capacity against heavy data center power requirements.
1. **Reducing embedded carbon footprints by extending hardware lifecycles** (10:50) — Delaying continuous physical hardware replacements actively minimizes the latent global carbon damage generated by complex electronic manufacturing lifecycles.
1. **Optimizing centralized cloud infrastructures for direct energy footprint reductions** (12:37) — Infrastructure cost-saving strategies completely parallel carbon reductions by actively shutting down abandoned instances and right-sizing underutilized virtual machine clusters.
1. **Interfacing open-source tooling for granular workload energy measurement** (15:28) — Integrating deep kernel diagnostic utilities enables dedicated telemetry metrics to trace power usage data down to singular operational applications.
1. **Tracking contextual software carbon intensities and service objectives** (16:59) — Application engineers can precisely calculate software carbon variance and outright halt automated build deployments if efficiency guidelines exceed tolerance thresholds.
1. **Carbon efficiency via automated schedule time and geographic shifting** (19:22) — Distributed systems naturally enable scheduling intensive batch jobs during optimal grid energy situations or directly moving workloads toward cleaner geographies.
1. **Analyzing cluster right-sizing and scaling experiments inside Kubernetes** (22:57) — Validating physical server node densities against scaled microservices discovers uniquely optimized configurations that drastically reduce wasted computational resources.
1. **Adapting carbon reduction systems for on-premises bare-metal configurations** (26:05) — Modifying automated public cloud methodologies to encompass localized infrastructure scaling and targeting internal architectural code bloat mitigates independent compute bottlenecks.

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