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

Modular data centers (MDCs) that can be placed right at the energy farms and powered mostly by renewable energy, is 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 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 uses the coefficient of variation metric to select the qualified renewable farms, it can identify a set of farms with complementary energy production patterns with a subgraph identification algorithm. 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 compared with existing approaches. SkyBox also minimizes the negative impact of the power variability on cloud applications, enabling it an effective solution of utilizing renewable energy for modern data centers.