Carbon and Reliability-Aware Computing for Heterogeneous Data Centers
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915221475426304 |
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| author | Zhang, Yichao Song, Yubo Sahoo, Subham |
| author_facet | Zhang, Yichao Song, Yubo Sahoo, Subham |
| contents | The rapid expansion of data centers (DCs) has intensified energy and carbon footprint, incurring a massive environmental computing cost. While carbon-aware workload migration strategies have been examined, existing approaches often overlook reliability metrics such as server lifetime degradation, and quality-of-service (QoS) that substantially affects both carbon and operational efficiency of DCs. Hence, this paper proposes a comprehensive optimization framework for spatio-temporal workload migration across distributed DCs that jointly minimizes operational and embodied carbon emissions while complying with service-level agreements (SLA). A key contribution is the development of an embodied carbon emission model based on servers' expected lifetime analysis, which explicitly considers server heterogeneity resulting from aging and utilization conditions. These issues are accommodated using new server dispatch strategies, and backup resource allocation model, accounting hardware, software and workload-induced failure. The overall model is formulated as a mixed-integer optimization problem with multiple linearization techniques to ensure computational tractability. Numerical case studies demonstrate that the proposed method reduces total carbon emissions by up to 21%, offering a pragmatic approach to sustainable DC operations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_00518 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Carbon and Reliability-Aware Computing for Heterogeneous Data Centers Zhang, Yichao Song, Yubo Sahoo, Subham Systems and Control Computational Engineering, Finance, and Science Distributed, Parallel, and Cluster Computing Performance The rapid expansion of data centers (DCs) has intensified energy and carbon footprint, incurring a massive environmental computing cost. While carbon-aware workload migration strategies have been examined, existing approaches often overlook reliability metrics such as server lifetime degradation, and quality-of-service (QoS) that substantially affects both carbon and operational efficiency of DCs. Hence, this paper proposes a comprehensive optimization framework for spatio-temporal workload migration across distributed DCs that jointly minimizes operational and embodied carbon emissions while complying with service-level agreements (SLA). A key contribution is the development of an embodied carbon emission model based on servers' expected lifetime analysis, which explicitly considers server heterogeneity resulting from aging and utilization conditions. These issues are accommodated using new server dispatch strategies, and backup resource allocation model, accounting hardware, software and workload-induced failure. The overall model is formulated as a mixed-integer optimization problem with multiple linearization techniques to ensure computational tractability. Numerical case studies demonstrate that the proposed method reduces total carbon emissions by up to 21%, offering a pragmatic approach to sustainable DC operations. |
| title | Carbon and Reliability-Aware Computing for Heterogeneous Data Centers |
| topic | Systems and Control Computational Engineering, Finance, and Science Distributed, Parallel, and Cluster Computing Performance |
| url | https://arxiv.org/abs/2504.00518 |