Carbon-Aware Computing for Datacenters

Radovanović, Ana; Koningstein, Ross; Schneider, Ian; Chen, Bokan; Duarte, Alexandre; Roy, Binz; Xiao, Diyue; Haridasan, Maya; Hung, Patrick C. K.; Care, Nick; Talukdar, Saurav; Mullen, Eric; Smith, Kendal; Cottman, MariEllen; Cirne, Walfredo

doi:10.1109/tpwrs.2022.3173250

Cited by 83 publications

(40 citation statements)

References 32 publications

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“…The PDU-level CPU usage of serving jobs (category 1) in Google datacenters have predictable daily patterns. On the other hand, the total PDU-level CPU usage of batch processes has considerable intraday variability and their intraday profiles are hard to accurately predict [9]. As a result, the total PDU-level CPU usage typically varies significantly within a day with fluctuations larger than 10% for more than half of the PDUs across the Google fleet.…”

Section: B Datacenter Workloads and Their Managementmentioning

confidence: 99%

“…1) Per-PDU Power Model: To enable power efficiency and carbon-aware workload management as in [9], we use a Extensive analysis of 5-minute average PDU power consumption u PDU POW , as a function of its average CPU usage u PDU CPU , indicates three distinct utilization regimes (Figure 3). Furthermore, PDU power consumption monotonically increases with its CPU usage.…”

Section: Power Modelsmentioning

confidence: 99%

“…The Per-PDU model's piecewise linearity provides a suitable framework to evaluate the impact of changes in CPU usage on power usage, which can then be easily integrated into cost-and carbon-aware optimizations designed to shape compute load by shifting compute tasks in time and space ( [9], [17], [18]), i.e., between datacenter clusters. However, while the discussed models have comparable MAPEs when predicting changes in power usage as a result of small variations in CPU usage, this is not the case when changes in CPU Cluster level performance of the proposed models for eight experimental clusters during the planned power drop event.…”

Section: B Use Case #2: Load Shapingmentioning

confidence: 99%

“…During summer months of 2021, Test II was conducted to evaluate Google datacenters potential to drop power for 6 hours on selected days: 7/13, 7/29, 7/30, 7/31, 8/11. The power load was dropped by enforcing lower cluster-level CPU consumption using the capacity curve mechanism that leverages temporal flexibility of some of compute workloads, as recently proposed in [9]. The load shedding was accomplished in two stages lasting 2 and 4 hours to avoid negative performance implications on workloads due to the abrupt and significant drops in available CPU capacity.…”

Section: B Use Case #2: Load Shapingmentioning

confidence: 99%

“…Each model shows advantages in certain use cases. For example, the Per-PDU model's predictive performance across all CPU/power usage regimes and its piecewise-linearity is exploited within Google's Carbon-Intelligent Computing System [9] to evaluate the impact of changes in datacenter clusters' CPU usage on their power consumption as a result of shifting workloads in time and across locations. The Per-PDU model has also been used for estimation and real-life validation of a datacenter's potential to reduce power consumption during grid-level demand response events.…”

mentioning

confidence: 99%

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Power Modeling for Effective Datacenter Planning and Compute Management

Radovanović

Chen

Talukdar

et al. 2022

IEEE Trans. Smart Grid

Self Cite

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Over the past decade, there has been a global growth in datacenter capacity, power consumption and the associated costs. Accurate mapping of datacenter resource usage (CPU, RAM, etc.) and hardware configurations (servers, accelerators, etc.) to its power consumption is necessary for efficient long-term infrastructure planning and real-time compute load management. This paper presents two types of statistical power models that relate CPU usage of Google's Power Distribution Units (PDUs, commonly referred to as power domains) to their power consumption. The models are deployed in production and are used for cost-and carbon-aware load management, power provisioning and infrastructure rightsizing. They are simple, interpretable and exhibit uniformly high prediction accuracy in modeling power domains with large diversity of hardware configurations and workload types across Google fleet. A multi-year validation of the deployed models demonstrate that they can predict power with less than 5% Mean Absolute Percent Error (MAPE) for more than 95% diverse PDUs across Google fleet. This performance matches the best reported accuracies coming from studies that focus on specific workload types, hardware platforms and, typically, more complex statistical models.

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Section: B Datacenter Workloads and Their Managementmentioning

confidence: 99%

Section: Power Modelsmentioning

confidence: 99%

Section: B Use Case #2: Load Shapingmentioning

confidence: 99%

Section: B Use Case #2: Load Shapingmentioning

confidence: 99%

mentioning

confidence: 99%

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Power Modeling for Effective Datacenter Planning and Compute Management

Radovanović

Chen

Talukdar

et al. 2022

IEEE Trans. Smart Grid

Self Cite

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Software‐in‐the‐loop simulation for developing and testing carbon‐aware applications

Wiesner,

Steinke,

Nickel

et al. 2023

Softw Pract Exp

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The growing electricity demand of IT infrastructure has raised significant concerns about its carbon footprint. To mitigate the associated emissions of computing systems, current efforts therefore increasingly focus on aligning the power usage of software with the availability of clean energy. To operate, such carbon‐aware applications require visibility and control over relevant metrics and configurations of the energy system. However, research and development of novel energy system abstraction layers and interfaces remain difficult due to the scarcity of available testing environments: Real testbeds are expensive to build and maintain, while existing simulation testbeds are unable to interact with real computing systems. To provide a widely applicable approach for developing and testing carbon‐aware software, we propose a method for integrating real applications into a simulated energy system through software‐in‐the‐loop simulation. The integration offers an API for accessing the energy system, while continuously modeling the computing system's power demand within the simulation. Our system allows for the integration of physical as well as virtual compute nodes, and can help accelerate research on carbon‐aware computing systems in the future.

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Cucumber: Renewable-Aware Admission Control for Delay-Tolerant Cloud and Edge Workloads

Wiesner

Scheinert

Wittkopp

et al. 2022

Euro-Par 2022: Parallel Processing

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The growing electricity demand of cloud and edge computing increases operational costs and will soon have a considerable impact on the environment. A possible countermeasure is equipping IT infrastructure directly with on-site renewable energy sources. Yet, particularly smaller data centers may not be able to use all generated power directly at all times, while feeding it into the public grid or energy storage is often not an option. To maximize the usage of renewable excess energy, we propose Cucumber, an admission control policy that accepts delaytolerant workloads only if they can be computed within their deadlines without the use of grid energy. Using probabilistic forecasting of computational load, energy consumption, and energy production, Cucumber can be configured towards more optimistic or conservative admission. We evaluate our approach on two scenarios using real solar production forecasts for Berlin, Mexico City, and Cape Town in a simulation environment. For scenarios where excess energy was actually available, our results show that Cucumber's default configuration achieves acceptance rates close to the optimal case and causes 97.0 % of accepted workloads to be powered using excess energy, while more conservative admission results in 18.5 % reduced acceptance at almost zero grid power usage.

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Carbon-Aware Computing for Datacenters

Cited by 83 publications

References 32 publications

Power Modeling for Effective Datacenter Planning and Compute Management

Power Modeling for Effective Datacenter Planning and Compute Management

Software‐in‐the‐loop simulation for developing and testing carbon‐aware applications

Cucumber: Renewable-Aware Admission Control for Delay-Tolerant Cloud and Edge Workloads

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