## 1 Introduction
Their scheduler coordinates batch processing and dynamic voltage frequency scaling (DVFS) to effectively respond to workload fluctuations, achieving a 28% reduction in energy consumption compared to its competitors while meeting latency service-level objectives. [PERSON] et al. [13] introduce a novel framework, Morak (a multi-knob resource management framework), that conducts GPU resource partitioning with constraints on power and latency. Their framework optimizes GPU frequency scaling and deep neural network (DNN) model training batch sizing without violating the constraints, improving throughput by about 68% compared to other state-of-the-art baselines. [PERSON] et al. [14] investigate the potential of renewable energy for efficient deep neural network model inference job processing. They focus on the fluctuation of power generation in renewable energy sources, presenting various results of inference throughput according to DNN model types and renewable energy sources (hydroelectric, solar, and wind). Although the aforementioned studies provide impressive insights and methodologies, they lack sophisticated procedures for utilizing renewable energy in DL job processing within modern clusters. These studies fail to account for the explicit deadlines of DNN model training. Additionally, their heuristic-based methods do not guarantee theoretically optimal decision making in DL job management. In this paper, we introduce a novel Renewable-Aware Frequency Scaling (RA-FS) approach for energy-efficient DL job processing in GPU-based clusters. Our approach focuses on reducing energy consumption costs during DL job processing by harnessing renewable energy generation while meeting the performance requirements of diverse DL job requests. The key technical contributions of our work are as follows:  
* We design a GPU core and memory frequency scaling approach to optimize performance in real-time, tailored to the characteristics of DL job requests and assigned GPU devices. This FS-based approach is universally applicable across different GPU architectures. Even with minimal profiling data, we can determine model coefficients of FS for sophisticated performance tuning. * Our approach includes an optimization model that integrates renewable energy generation with DL job performance requirements. We introduce novel metrics: Deep Learning Job Requirement (DJR) and Deep Learning Job Completion per Slot (DJCS) to quantify service quality efficiently. These metrics enable us to easily maximize performance requirement satisfaction. Additionally, we employ a gated recurrent unit (GRU)-based predictor to forecast the irregular energy generation from renewable sources. * We present a log-transformation-based re-formulation technique to address our non-convex problem, which involves an energy consumption term defined by the product of power and training time. This technique transforms the non-convex problem into a convex one, ensuring an accurate optimal solution. * Utilizing a range of NVIDIA GPU devices, including those with the Ada Lovelace architecture (for example, RTX 4090), we collect data on real-world training performance and energy consumption at different GPU core and memory frequency settings. Additionally, by considering multiple regions of renewable energy generation, we develop practical experimental scenarios. These scenarios are designed to demonstrate the effectiveness of our RA-FS approach, highlighting its advantages over recent works in the field. This paper is structured as follows. Section 2 outlines the framework architecture that incorporates our proposed RA-FS approach. Section 3 details the system model, including the deep learning job model and the GPU energy consumption model. In Section 4, we define our primary optimization problem and discuss its re-formulation based on the log-transformation technique. Section 5 presents preliminary experimental results, showcasing the epoch completion times and energy consumption for DL job processing on NVIDIA GPU-based devices.