The field of computer science is witnessing significant advancements in efficient computing and storage systems. Recent developments focus on optimizing performance, reducing latency, and improving resource utilization in various applications, including machine learning, deep learning, and large-scale data processing. Researchers are exploring innovative approaches to address the challenges posed by emerging technologies, such as solid-state drives, graphics processing units, and distributed file systems. Notably, techniques like fair queueing, asynchronous I/O, and reinforcement learning are being applied to enhance system efficiency and scalability. These advancements have the potential to transform the way we design and interact with computing systems, enabling faster, more reliable, and more efficient processing of complex workloads. Noteworthy papers include: MQFQ-Sticky, which presents a fair queueing approach for GPU acceleration in serverless computing, reducing function latency by 2x to 20x. FlashANNS, a GPU-accelerated approximate nearest neighbor search system, achieving 2.3-5.9x higher throughput compared to state-of-the-art methods. Past-Future Scheduler, which precisely estimates peak memory resources required by large language models, resulting in up to 2-3x higher goodput under heavy loads.