Recently, many deep learning models have been trained in geographically distributed data centers. The carbon emissions produced by training the models may pose a significant threat to climate change like increasing temperatures. Existing studies have a hardship in shifting the workload of training models to a data center with low carbon emissions. So, they fail to ensure low emissions of the workload during training, especially when long-term workloads like Large Language Models (LLMs) are trained. To cope with this problem, we propose a method that shifts the workload to a cloud with low carbon emissions while enduring a lack of computational resources. Specifically, we define a task scheduler that includes states and their transitions to migrate mini-batches dynamically. Next, we present a fault-tolerant control that optimizes a GPU frequency to adapt to workload variations of training models while guaranteeing its power consumption. Last, we conducted exhaustive experiments using real-world data in terms of carbon emissions, transfer time, and power consumption compared to state-of-the-art methods.

Data centers frequently face significant memory under-utilization due to factors such as infrastructure overprovisioning, inefficient workload scheduling, and limited server configurations. This paper introduces Agile-DRAM, a novel DRAM architecture that addresses this issue by flexibly converting the under-utilized memory capacity into enhanced latency performance and reduced power consumption. Through minor modifications to the conventional DRAM architecture, Agile-DRAM supports multiple operational modes: low-latency, lowpower, and the default max-capacity mode. Notably, Agile-DRAM facilitates agile transitions between these modes in response to workload fluctuations in data centers at runtime. Evaluation results demonstrate that the low-latency mode can boost singlecore execution speed by up to 25.8% and diminish energy usage by up to 22.4%. Similarly, the low-power mode can reduce DRAM standby and self-refresh power by 31.6% and 85.7%, respectively.