Nov 30, 2020 · K-ear classifies data into five seasonal categories according to their seasonal access characteristics and then classifies every seasonal category into three ...

K-ear: Extracting Data Access Periodic Characteristics for Energy-aware Data Clustering and Storing in Cloud. Storage Systems. Xindong You 1, Tian Sun1, Dawei ...

K‐ear: Extracting data access periodic characteristics for energy‐aware data clustering and storing in cloud storage systems · Xindong You, Tian Sun, +3 authors

Oct 22, 2024 · K-ear [23] categorizes data into multiple groups by extracting seasonal period characteristics using the K-means clustering algorithm. These ...

Rapid increase in energy consumption is a serious problem in cloud storage systems. Data accessed in large-scale storage systems usually exhibit tempo.

Data accessed in large‐scale storage systems usually exhibit temporal and spatial characteristics, which make it possible to reduce energy consumption by ...

K-ear: Extracting data access periodic characteristics for energy-aware data clustering and storing in cloud storage systems. Concurr. Comput. Pract. Exp ...

K-ear [23] categorizes data into multiple groups by extracting seasonal period characteristics using the K-means clustering algorithm. These categories are then ...

K‐ear: Extracting data access periodic characteristics for energy‐aware data clustering and storing in cloud storage systems ... Concurr. Comput. Pract. Exp. 2021.

Jan 29, 2024 · K-ear [23] categorizes data into multiple groups by extracting seasonal period characteristics using the K-means clustering algorithm. These ...