TY  - JOUR
A1  - Peng, Junjie
A1  - Liu, Danxu
A1  - Wang, Yingtao
A1  - Zeng, Ying
A1  - Cheng, Feng
A1  - Zhang, Wenqiang
T1  - Weight-based strategy for an I/O-intensive application at a cloud data center
JF  - Concurrency and computation : practice & experience
N2  - Applications with different characteristics in the cloud may have different resources preferences. However, traditional resource allocation and scheduling strategies rarely take into account the characteristics of applications. Considering that an I/O-intensive application is a typical type of application and that frequent I/O accesses, especially small files randomly accessing the disk, may lead to an inefficient use of resources and reduce the quality of service (QoS) of applications, a weight allocation strategy is proposed based on the available resources that a physical server can provide as well as the characteristics of the applications. Using the weight obtained, a resource allocation and scheduling strategy is presented based on the specific application characteristics in the data center. Extensive experiments show that the strategy is correct and can guarantee a high concurrency of I/O per second (IOPS) in a cloud data center with high QoS. Additionally, the strategy can efficiently improve the utilization of the disk and resources of the data center without affecting the service quality of applications.
KW  - IOPS
KW  - process scheduling
KW  - random I
KW  - O
KW  - small files
KW  - weight
Y1  - 2018
U6  - https://doi.org/10.1002/cpe.4648
SN  - 1532-0626
SN  - 1532-0634
VL  - 30
IS  - 19
PB  - Wiley
CY  - Hoboken
ER  - 
TY  - JOUR
A1  - Wang, Cheng
A1  - Yang, Haojin
A1  - Meinel, Christoph
T1  - Image Captioning with Deep Bidirectional LSTMs and Multi-Task Learning
JF  - ACM transactions on multimedia computing, communications, and applications
N2  - Generating a novel and descriptive caption of an image is drawing increasing interests in computer vision, natural language processing, and multimedia communities. In this work, we propose an end-to-end trainable deep bidirectional LSTM (Bi-LSTM (Long Short-Term Memory)) model to address the problem. By combining a deep convolutional neural network (CNN) and two separate LSTM networks, our model is capable of learning long-term visual-language interactions by making use of history and future context information at high-level semantic space. We also explore deep multimodal bidirectional models, in which we increase the depth of nonlinearity transition in different ways to learn hierarchical visual-language embeddings. Data augmentation techniques such as multi-crop, multi-scale, and vertical mirror are proposed to prevent over-fitting in training deep models. To understand how our models "translate" image to sentence, we visualize and qualitatively analyze the evolution of Bi-LSTM internal states over time. The effectiveness and generality of proposed models are evaluated on four benchmark datasets: Flickr8K, Flickr30K, MSCOCO, and Pascal1K datasets. We demonstrate that Bi-LSTM models achieve highly competitive performance on both caption generation and image-sentence retrieval even without integrating an additional mechanism (e.g., object detection, attention model). Our experiments also prove that multi-task learning is beneficial to increase model generality and gain performance. We also demonstrate the performance of transfer learning of the Bi-LSTM model significantly outperforms previous methods on the Pascal1K dataset.
KW  - Deep learning
KW  - LSTM
KW  - multimodal representations
KW  - image captioning
KW  - mutli-task learning
Y1  - 2018
U6  - https://doi.org/10.1145/3115432
SN  - 1551-6857
SN  - 1551-6865
VL  - 14
IS  - 2
PB  - Association for Computing Machinery
CY  - New York
ER  -