Pith. sign in

REVIEW 1 cited by

Advancing Environmental Sustainability in Data Centers via Carbon Depreciation Models

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2403.04976 v4 pith:HM72VKJZ submitted 2024-03-08 cs.DC

classification cs.DC
keywords carbonhardwaredepreciationembodiedenergyjobsolderprovisioning
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Recent improvements in energy efficiency and renewable energy integration have increased the relative importance of embodied carbon in data centers, motivating improved provisioning strategies. Conventional approaches primarily minimize operational energy, but this perspective is increasingly insufficient for sustainability. In this paper, we propose carbon depreciation models to encourage longer hardware lifetimes. Carbon depreciation assigns a larger portion of embodied carbon to newly provisioned servers, discouraging unnecessary deployment of new hardware. As a result, new servers are provisioned mainly for jobs with strict quality-of-service (QoS) constraints, while older servers, whose embodied carbon has largely been recovered, are used for other workloads. We further argue that both embodied carbon and operational carbon from server idle time should be recovered during active jobs, encouraging provisioning strategies that maintain high utilization. We show that prior carbon accounting strategies can be counterproductive: under a greedy scheduler minimizing carbon under QoS constraints, jobs are priced as 25% cheaper on new hardware than on older hardware. In contrast, our approach uses a greedy scheduler that prioritizes older hardware through non-linear carbon depreciation, promoting sustainable provisioning. Experimental results show carbon reductions of 28-57%, depending on server lifetime assumptions.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. EcoServe: Designing Carbon-Aware AI Inference Systems

    cs.DC 2025-02 conditional novelty 6.0 of 10

    EcoServe combines four strategies (reuse, rightsize, reduce, recycle) in an ILP optimizer to cut modeled carbon emissions for LLM serving by up to 47% while keeping SLOs.

Pith tools