HRADP: A Heat-Recirculation-Aware Data Placement Approach for Energy-Efficient Cloud Data Centers

With the proliferation of cloud computing, the exponential growth of data amount requires continuous expansion of storage capacity to meet the storage demand, consequently resulting in higher energy consumption in data centers. However, many of the conventional data placement strategies strive to save energy by optimizing the distribution of data requests, while overlooking the impact of heat recirculation among data nodes. To bridge this gap, we propose a heat-recirculation-aware data placement approach, termed HRADP, designed to optimize data placement in data centers while reducing energy consumption. First, based on the extent of heat recirculation, HRADP places data to the upper limit of the disk allowance for each data node. Second, during energy allocation, HRADP further eliminates disks with high current workloads to prevent localized hotspots. Finally, during request scheduling, HRADP selects dormant data nodes according to the request size, minimizing the startup energy consumption caused by data nodes waking up with small requests. We implement the HRADP approach on a data center simulation platform, CloudSim, and its performance with state-of-the-art, including the Energy-efficient and Thermal-aware Data Placement (ETDP) algorithm, Thermal-aware file assignment technique (TIGER), Storage and rack-sensitive replica placement algorithm (SRS), and Hadoop Distributed File System (HDFS). The experimental results reveal that HRADP revamps the cooling supply temperature, total energy, and data throughput by averages of 0.08%-0.6%, 8.22%-53.03%, and 9.58%-64.59%, respectively.