Exploring the Efficiency of Renewable Energy-based Modular Data Centers at Scale

TL;DR

SkyBox tackles renewable energy variability in modular data centers by colocating rMDCs with a carefully selected set of renewable farms and optimizing workload placement and migrations with a mixed-integer program. It uses a coefficient-of-variation-based site identification, forms small complementary subgraphs to stabilize aggregated power, and migrates VMs to mitigate power fluctuations while minimizing non-renewable usage. Evaluation on real power traces and VM workloads shows substantial carbon-footprint reductions (~46%), cost savings, and robustness to mispredictions, scaling to large rMDC deployments. The approach offers a practical path toward near-zero-emission cloud infrastructure by reducing grid reliance and embodied carbon through strategic siting and optimization.

Abstract

Modular data centers (MDCs) that can be placed right at the energy farms and powered mostly by renewable energy, are proven to be a flexible and effective approach to lowering the carbon footprint of data centers. However, the main challenge of using renewable energy is the high variability of power produced, which implies large volatility in powering computing resources at MDCs, and degraded application performance due to the task evictions and migrations. This causes challenges for platform operators to decide the MDC deployment. To this end, we present SkyBox, a framework that employs a holistic and learning-based approach for platform operators to explore the efficient use of renewable energy with MDC deployment across geographical regions. SkyBox is driven by the insights based on our study of real-world power traces from a variety of renewable energy farms -- the predictable production of renewable energy and the complementary nature of energy production patterns across different renewable energy sources and locations. With these insights, SkyBox first uses the coefficient of variation metric to select the qualified renewable farms, and proposes a subgraph identification algorithm to identify a set of farms with complementary energy production patterns. After that, SkyBox enables smart workload placement and migrations to further tolerate the power variability. Our experiments with real power traces and datacenter workloads show that SkyBox has the lowest carbon emissions in comparison with current MDC deployment approaches. SkyBox also minimizes the impact of the power variability on cloud virtual machines, enabling rMDCs a practical solution of efficiently using renewable energy.

Figures (19)

• Figure 1: The target system architecture of SkyBox. It facilitates the deployment of renewable energy-based modular data centers (rMDCs) across multiple geographical regions at scale.
• Figure 2: Power variation over time in renewable energy farms.
• Figure 3: Reducing the variability in renewable energy production by aggregating multiple sites.
• Figure 4: Energy prediction of solar and wind in near (3-hour and day-ahead) and far-away future (week-ahead).
• Figure 5: System overview of SkyBox.