Continuous data scale growth increases energy consumption and operating cost that cannot be ignored in cloud storage systems. Previous studies have shown that analyzing the characteristics of I/O access and mining data features is effective for reasonable data distribution in storage systems. The granularity and criterion of classification are the key factors in determining the data distribution. To decrease energy consumption and operating cost, this paper puts forward a fine-grained framework of the climatic-season-based energy-aware in cloud storage system called CSEA. The framework concludes the following three aspects: (i) data feature mining. CSEA discovers potential data features by analyzing data access to provide help with data classification. (ii) K-means clustering algorithm. CSEA uses an unsupervised data classification algorithm in machine learning to divide data into categories based on seasonal characteristics by gathering real I/O access. (iii) data distribution of fine-grained. On the basis of seasonal features, CSEA fuses regional features to further refine the data distribution granularity to save on energy consumption and operating cost. Simulation experiments using extended CloudSimDisk and the constructed mathematical models indicate that CSEA reduces the energy consumption and operating cost compared with the single data classification standard and coarse-grained data distribution.

Thanks to excellent reliability, availability, flexibility and scalability, redundant arrays of independent (or inexpensive) disks (RAID) are widely deployed in large-scale data centers. RAID scaling effectively relieves the storage pressure of the data center and increases both the capacity and I/O parallelism of storage systems. To regain load balancing among all disks including old and new, some data usually are migrated from old disks to new disks. Owing to unique parity layouts of erasure codes, traditional scaling approaches may incur high migration overhead on RAID-6 scaling. This paper proposes an efficient approach based Short-Code for RAID-6 scaling. The approach exhibits three salient features: first, SS6 introduces $\tau $ to determine where new disks should be inserted. Second, SS6 minimizes migration overhead by delineating migration areas. Third, SS6 reduces the XOR calculation cost by optimizing parity update. The numerical results and experiment results demonstrate that (i) SS6 reduces the amount of data migration and improves the scaling performance compared with Round-Robin and Semi-RR under offline, (ii) SS6 decreases the total scaling time against Round-Robin and Semi-RR under two real-world I/O workloads (iii) the user average response time of SS6 is better than the other two approaches during scaling and after scaling.

Recently, head detection has been widely used in target detection, which has a great application value for improving security prevention and control in public places, as well as enhancing target tracking and identification in national defense, criminal investigation, and other fields. However, detecting small targets accurately at long distances is very difficult, and current methods often lack optimization of multi‐resolution features. Therefore, the authors propose a one‐stage detection network CFNet (cross‐layer feature fusion and fusion weight attention network), in which a fusion weight attention mechanism module (FWAM) is proposed to give different weights to the fused features in order to distinguish the importance of different features. The module increases the weights of features that contain strong information so that the fused features are focused on feature points that are beneficial for optimal head detection. Meanwhile, a cross‐layer feature fusion module is proposed to fuse information from different resolution feature maps to compensate for the decrease in detection accuracy caused by the omission of information features at low resolution, and a connection network for contextual information fusion is constructed, while weight parameter value settings are introduced to optimize the detection effect after fusion of different resolution features. In order to better reflect the effectiveness of the network, the experiments are performed on the SCUT‐HEAD PartA dataset and the Brainwash dataset; the results show that the network the authors proposed is better than the existing comparison methods, which proves the robustness and effectiveness of the network.