K-ear: Extracting data access periodic characteristics for energy-aware data clustering and storing in cloud storage systems

Abstract

Rapid increase in energy consumption is a serious problem in cloud storage systems. Data accessed in large-scale storage systems usually exhibit temporal and spatial characteristics, which make it possible to reduce energy consumption by clustering data with similar access characteristics for storage in the same zone of cloud storage systems. Existing works usually only focus on the frequency of data access. However, widely existing phenomena show data access with seasonal and tidal characteristics in cloud storage systems. The seasonal and tidal characteristics of data access are extracted thoroughly in this paper. According to the extracted data access characteristics, energy-aware data cl..