Carbon-Aware Temporal Data Transfer Scheduling Across Cloud Datacenters
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866913875162562560 |
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| author | Rodrigues, Elvis Goldverg, Jacob Kosar, Tevfik |
| author_facet | Rodrigues, Elvis Goldverg, Jacob Kosar, Tevfik |
| contents | Inter-datacenter communication is a significant part of cloud operations and produces a substantial amount of carbon emissions for cloud data centers, where the environmental impact has already been a pressing issue. In this paper, we present a novel carbon-aware temporal data transfer scheduling framework, called LinTS, which promises to significantly reduce the carbon emission of data transfers between cloud data centers. LinTS produces a competitive transfer schedule and makes scaling decisions, outperforming common heuristic algorithms. LinTS can lower carbon emissions during inter-datacenter transfers by up to 66% compared to the worst case and up to 15% compared to other solutions while preserving all deadline constraints. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_04117 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Carbon-Aware Temporal Data Transfer Scheduling Across Cloud Datacenters Rodrigues, Elvis Goldverg, Jacob Kosar, Tevfik Distributed, Parallel, and Cluster Computing Networking and Internet Architecture Inter-datacenter communication is a significant part of cloud operations and produces a substantial amount of carbon emissions for cloud data centers, where the environmental impact has already been a pressing issue. In this paper, we present a novel carbon-aware temporal data transfer scheduling framework, called LinTS, which promises to significantly reduce the carbon emission of data transfers between cloud data centers. LinTS produces a competitive transfer schedule and makes scaling decisions, outperforming common heuristic algorithms. LinTS can lower carbon emissions during inter-datacenter transfers by up to 66% compared to the worst case and up to 15% compared to other solutions while preserving all deadline constraints. |
| title | Carbon-Aware Temporal Data Transfer Scheduling Across Cloud Datacenters |
| topic | Distributed, Parallel, and Cluster Computing Networking and Internet Architecture |
| url | https://arxiv.org/abs/2506.04117 |