Hammer: Towards Efficient Hot-Cold Data Identification via Online Learning

In the contemporary landscape of big data and cloud computing, the efficient management of storage resources has emerged as a paramount concern. One of the most critical aspects of this challenge is the accurate identification of data's "cold" and "hot" states. Data is classified as "hot" if it is frequently accessed, necessitating fast and readily available storage solutions. Conversely, "cold" data, which is rarely accessed, can be stored more cost-effectively in slower, less expensive storage mediums. Effective hot-cold identification not only optimizes storage costs but also enhances system performance by ensuring that the most relevant data is quickly accessible[1, 2, 3].