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

TL;DR

This paper introduces SkyBox, a learning-based framework that optimizes renewable energy-based modular data center deployment and workload management across regions to reduce carbon emissions and handle power variability effectively.

Contribution

SkyBox provides a novel, holistic approach combining power trace analysis, farm selection, and workload migration to improve renewable energy utilization in modular data centers.

Findings

SkyBox achieves the lowest carbon emissions compared to existing approaches.

It effectively minimizes workload disruptions caused by renewable energy variability.

The framework demonstrates practical viability with real-world power traces.

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.