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To represent the entire carbon footprint of computing devices, carbon metrics often include both an embodied cost (i.e., carbon cost to produce the device) and an operational cost (i.e., carbon cost to run the device). The embodied carbon cost is typically high, but it is amortized over the lifetime of the device. In this vision statement, we argue that for carbon metrics to be useful, we need (i) accurate metrics for lifetime, which are challenging for SSDs, and (ii) correct reasoning about carbon costs when using such metrics.
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The Sunk Carbon Fallacy: Rethinking Carbon Footprint Metrics for Effective Carbon-Aware Scheduling
The rapid increase in computing demand and corresponding energy consumption have focused attention on computing's impact on the climate and sustainability. Prior work proposes metrics that quantify computing's carbon footprint across several lifecycle phases, including its supply chain, operation, and end-of-life. Industry uses these metrics to optimize the carbon footprint of manufacturing hardware and running computing applications. Unfortunately, prior work on optimizing datacenters' carbon footprint often succumbs to the sunk cost fallacy by considering embodied carbon emissions (a sunk cost) when making operational decisions (i.e., job scheduling and placement), which leads to operational decisions that do not always reduce the total carbon footprint. In this paper, we evaluate carbon-aware job scheduling and placement on a given set of servers for several carbon accounting metrics. Our analysis reveals state-of-the-art carbon accounting metrics that include embodied carbon emissions when making operational decisions can increase the total carbon footprint of executing a set of jobs. We study the factors that affect the added carbon cost of such suboptimal decision-making. We then use a real-world case study from a datacenter to demonstrate how the sunk carbon fallacy manifests itself in practice. Finally, we discuss the implications of our findings in better guiding effective carbon-aware scheduling in on-premise and cloud datacenters.
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- PAR ID:
- 10591309
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9798400712869
- Page Range / eLocation ID:
- 542 to 551
- Format(s):
- Medium: X
- Location:
- Redmond WA USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3698038.3698542
