Thermal Modeling and Management of Storage Systems

Abstract

Energy consumption of data storage systems has increased significantly for the past decades. There is an urgent need to build energy-efficient data storage systems. Computing cost of IT facilities and cooling cost of air conditioners contribute to a large portion of the total energy consumption of data centers. A large amount of researchers focus on reducing the computing cost by balancing workload or powering off idle data nodes to save energy. In recent years, growing attention has been paid to decreasing the cooling cost. Temperature is a major contributor to cooling cost, and thermal management has become a popular topic in building energy-efficient data centers. Extensive research of thermal impacts of processors and memories has been presented in literature, however, the thermal impacts of disks have not been fully investigated. In this dissertation, experiments are conducted to characterize the thermal behavior of processors and disks by using real-world benchmarks (e.g., postmark and whetstone). The profiling results show that disks have comparable thermal impacts as processors to overall temperature of a data node. Then, we develop an approach to generate thermal models for estimating temperatures of processors, disks, and data nodes. We validate the thermal models by comparing the predictions with real measurements by temperature sensors deployed on data nodes. We further propose an energy model to estimate the total energy cost of data nodes. Finally, by applying our thermal and energy models, we propose thermal management strategies for building energy-efficient data centers. These strategies include a thermal-aware task scheduling strategy, thermal-aware data placement strategies for homogeneous and hybrid storage clusters, and a predictive thermal-aware data transmission strategy.