Power forecasting has a guiding effect on power-aware scheduling strategies to reduce unnecessary power consumption in data centers. Many metrics related to power consumption can be collected in physical servers, such as the status of CPU, memory, and other components. However, most existing methods empirically exploit a small number of metrics to forecast power consumption. To this end, this article uses feature selection based on causality to explore the metrics that strongly influence the power consumption of different tasks. Moreover, we propose a tensor-based dual-stage attention LSTM to forecast the non-linear and non-periodic power consumption. In the proposed model, a multi-way delay embedding transform is utilized to convert the time series into tensors along the temporal direction. The LSTM combines with the tensor technique and the attention mechanism to capture the temporal pattern effectively. In addition, we adopt the cost-sensitive loss function to optimize the specific power forecasting problem in data centers. The experimental results demonstrate that our method can achieve up to 1.4% to 4.3% forecasting accuracy improvement compared with the state-of-the-art models.

Accurate and real-time detection of airplanes, cars, and ships in remote sensing images is an important but challenging task that plays an important role in both military and civilian life. The most challenging issues posed by this task are the intensive and tiny size of objects and the complexity of application scenarios. In this letter, we propose a multi-vision small object detector that can rapidly and accurately detect airplanes, cars, and ships in remote sensing images. We make the following three contributions: a multiscale residual block (MRB) is proposed, whereby dilated convolution is employed in a cascade residual block to capture multiscale context information, thus improving the feature representation ability of convolutional neural networks; a multiscale receptive field enhancement module (MRFEM) is proposed that combines features obtained using dilated convolution at different dilation rates to further enhance the multiscale feature representation of the remote sensing targets; and a multi-vision network (MVNet) is presented that uses multiple low-level feature maps with multi-branch convolution to detect small objects. Experimental results show that the proposed method can achieve a significant mean average precision (mAP) of 94.70% in remote sensing images and can run at 24 FPS on a single NVIDIA 1080Ti GPU.