Hydra: Hybrid Server Power Model
This work addresses power efficiency for data center operators by providing a more adaptable model, though it is incremental as it builds on existing power modeling approaches.
The paper tackles the problem of accurately modeling server power consumption in heterogeneous data centers by proposing Hydra, a hybrid model that dynamically selects the best power model based on server conditions, resulting in improved performance across all compute-intensity levels compared to state-of-the-art solutions.
With the growing complexity of big data workloads that require abundant data and computation, data centers consume a tremendous amount of power daily. In an effort to minimize data center power consumption, several studies developed power models that can be used for job scheduling either reducing the number of active servers or balancing workloads across servers at their peak energy efficiency points. Due to increasing software and hardware heterogeneity, we observed that there is no single power model that works the best for all server conditions. Some complicated machine learning models themselves incur performance and power overheads and hence it is not desirable to use them frequently. There are no power models that consider containerized workload execution. In this paper, we propose a hybrid server power model, Hydra, that considers both prediction accuracy and performance overhead. Hydra dynamically chooses the best power model for the given server conditions. Compared with state-of-the-art solutions, Hydra outperforms across all compute-intensity levels on heterogeneous servers.