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Data temperature is a response to the ever-growing amount of data. These data have to be stored, but they have been observed that only a small portion of the data are accessed more frequently at any one time. This leads to the concept of hot and cold data. Cold data can be migrated away from high-performance nodes to free up performance for higher priority data. Existing studies classify hot and cold data primarily on the basis of data age and usage frequency. We present this as a limitation in the current implementation of data temperature. This is due to the fact that age automatically assumes that all new data have priority and that usage is purely reactive. We propose new variables and conditions that influence smarter decision-making on what are hot or cold data and allow greater user control over data location and their movement. We identify new metadata variables and user-defined variables to extend the current data temperature value. We further establish rules and conditions for limiting unnecessary movement of the data, which helps to prevent wasted input output (I/O) costs. We also propose a hybrid algorithm that combines existing variables and new variables and conditions into a single data temperature. The proposed system provides higher accuracy, increases performance, and gives greater user control for optimal positioning of data within multi-tiered storage solutions.

The scale of data shows an explosive growth trend, with wide use of cloud storage. However, there are challenges such as network latency and energy consumption. The emergence of edge computing brings data close to the edge of the network, making it a good supplement to cloud computing. The spatiotemporal characteristics of data have been largely ignored in studies of data placement and storage optimization. To this end, a temperature matrix-based data placement method using an improved Hungarian algorithm (TEMPLIH) is proposed in this work. A temperature matrix is used to reflect the influence of data characteristics on its placement. A data replica matrix selection algorithm based on temperature matrix (RSA-TM) is proposed to meet latency requirements. Then, an improved Hungarian algorithm based on replica matrix (IHA-RM) is proposed, which satisfies the balance among the multiple goals of latency, cost, and load balancing. Compared with other data placement strategies, experiments show that the proposed method can effectively reduce the cost of data placement while meeting user access latency requirements and maintaining a reasonable load balance between edge servers. Further improvement is discussed and the idea of regional value is proposed. [ABSTRACT FROM AUTHOR]