arXiv subjects

Liuzixuan Lin

Publications and source records attributed to Liuzixuan Lin.

4 recordsLinked to original sources

arXiv2026

Slasher: Power Flexibility for Cloud Datacenters

Datacenters consume many megawatts of power, and regularly encounter scenarios that require modulating their power draw. These scenarios include datacenter infrastructure failures, power grid failures, grid services, and more, spanning a diverse range of requirements in terms of the power magnitude, the scope of the reduction, the notice time, and other dimensions. To address these scenarios, we have built Slasher, a general system for modulating the power of \azure datacenters to handle scenarios ranging from individual racks to regional multi-datacenter grid events. Slasher coordinates datacenter resources with the goal of meeting power targets while minimizing negative impact on hosted workloads. In this paper, we review the main power modulation scenarios, characterize the power reduction levers using data from production cloud datacenters, describe Slasher's system architecture, and formulate the cloud datacenter power modulation control problem. We also develop a high-fidelity datacenter simulator and propose a workload impact model, using them to design and evaluate power control algorithms.

cs.DC↗

arXiv2025

Distribution and Management of Datacenter Load Decoupling

The exploding power consumption of AI and cloud datacenters (DCs) intensifies the long-standing concerns about their carbon footprint, especially because DCs' need for constant power clashes with volatile renewable generation needed for grid decarbonization. DC flexibility (a.k.a. load adaptation) is a key to reducing DC carbon emissions by improving grid renewable absorption. DC flexibility can be created, without disturbing datacenter capacity by decoupling a datacenter's power capacity and grid load with a collection of energy resources. Because decoupling can be costly, we study how to best distribute and manage decoupling to maximize benefits for all. Key considerations include site variation and datacenter-grid cooperation. We first define and compute the power and energy needs of datacenter load decoupling, and then we evaluate designed distribution and management approaches. Evaluation shows that optimized distribution can deliver >98% of the potential grid carbon reduction with 70% of the total decoupling need. For management, DC-grid cooperation (2-way sharing and control vs. 1-way info sharing) enables 1.4x grid carbon reduction. Finally, we show that decoupling may be economically viable, as on average datacenters can get power cost and carbon emissions benefits greater than their local costs of decoupling. However, skew across sites suggests grid intervention may be required.

cs.DC↗

arXiv2023

Exploding AI Power Use: an Opportunity to Rethink Grid Planning and Management

The unprecedented rapid growth of computing demand for AI is projected to increase global annual datacenter (DC) growth from 7.2% to 11.3%. We project the 5-year AI DC demand for several power grids and assess whether they will allow desired AI growth (resource adequacy). If not, several "desperate measures" -- grid policies that enable more load growth and maintain grid reliability by sacrificing new DC reliability are considered. We find that two DC hotspots -- EirGrid (Ireland) and Dominion (US) -- will have difficulty accommodating new DCs needed by the AI growth. In EirGrid, relaxing new DC reliability guarantees increases the power available to 1.6x--4.1x while maintaining 99.6% actual power availability for the new DCs, sufficient for the 5-year AI demand. In Dominion, relaxing reliability guarantees increases available DC capacity similarly (1.5x--4.6x) but not enough for the 5-year AI demand. New DCs only receive 89% power availability. Study of other US power grids -- SPP, CAISO, ERCOT -- shows that sufficient capacity exists for the projected AI load growth. Our results suggest the need to rethink adequacy assessment and also grid planning and management. New research opportunities include coordinated planning, reliability models that incorporate load flexibility, and adaptive load abstractions.

cs.DC↗

arXiv2023

Adapting Datacenter Capacity for Greener Datacenters and Grid

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 entire day). We evaluate DC carbon emissions reduction. Results show that local coordination scope fails to reduce carbon emissions significantly (3.2%--5.4% reduction). Expanding coordination scope to a set of datacenters improves slightly (4.9%--7.3%). PlanShare, with grid-wide coordination and full-day capacity planning, performs the best. PlanShare reduces DC emissions by 11.6%--12.6%, 1.56x--1.26x better than the best local, online approach's results. PlanShare also achieves lower cost. We expect these advantages to increase as renewable generation in power grids increases. Further, a known full-day DC capacity plan provides a stable target for DC resource management.

cs.DC↗