System Software Lab

Research focus on Operating Systems, Cloud Computing, Network Systems, AI Infrastructure, and System Software at Korea University. AI has been transitioning to essential everyday infrastructure. System software determines whether large models are practical at scale—it controls how efficiently hardware resources are used, how predictable runtimes are, and how easily workloads can scale across multiple machines. For example, in recent years, datacenters have evolved to support diverse, heterogeneous workloads and GPU devices. One prominent example is distributed deep learning training, which enables the development of large-scale AI models such as GPT, DALL-E, and LLaMA. These models can involve more than 530 billion parameters and rely on hundreds of GPU nodes. At the same time, large language model (LLM) inference and serving have emerged as critical workloads that demand efficient KV-Cache management, request scheduling, and low-latency execution. As a result, optimizing infrastructure efficiency and utilization—both in training and inference—has become increasingly important. However, recent reports from major cloud providers such as Microsoft and Alibaba show that average GPU utilization rates are only 20–50%. Such low utilization indicates a significant waste of datacenter resources, underscoring the need for more effective systems strategies to improve efficiency. Our research aims to make AI systems—spanning training, inference, and serving—faster, more affordable, energy-efficient, and reliable as models and datasets continue to grow. System software for networking matters because modern computing is inherently connected. Every cloud service, data pipeline, and AI workload depends on timely and reliable communication. We enhance performance, isolation, and predic