Adapting Datacenter Capacity for Greener Datacenters and Grid
Liuzixuan Lin, Andrew A. Chien

TL;DR
This paper proposes a coordinated approach to adapt datacenter capacity for reducing carbon emissions, demonstrating that global, grid-wide planning significantly outperforms local strategies in emission reduction and cost savings.
Contribution
It introduces PlanShare, a novel capacity planning scheme that uses full-day, grid-wide coordination and information sharing to optimize datacenter capacity and reduce emissions.
Findings
PlanShare reduces datacenter emissions by up to 12.6%.
Global coordination outperforms local approaches in emission reduction.
Full-day planning provides stable capacity targets for datacenters.
Abstract
Cloud providers are adapting datacenter (DC) capacity to reduce carbon emissions. With hyperscale datacenters exceeding 100 MW individually, and in some grids exceeding 15% of power load, DC adaptation is large enough to harm power grid dynamics, increasing carbon emissions, power prices, or reduce grid reliability. To avoid harm, we explore coordination of DC capacity change varying scope in space and time. In space, coordination scope spans a single datacenter, a group of datacenters, and datacenters with the grid. In time, scope ranges from online to day-ahead. We also consider what DC and grid information is used (e.g. real-time and day-ahead average carbon, power price, and compute backlog). For example, in our proposed PlanShare scheme, each datacenter uses day-ahead information to create a capacity plan and shares it, allowing global grid optimization (over all loads, over…
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Taxonomy
TopicsDistributed and Parallel Computing Systems · Cloud Computing and Resource Management · Scientific Computing and Data Management
