Inter-datacenter (inter-DC) networks are essential for large enterprises to deliver high-quality services to end-users. Since DCs are vulnerable to natural disasters, an inter-DC network operator needs an effective emergency backup plan to evacuate the endangered data out in case of a progressive disaster whose status can be predicted by an early warning system. In this paper, we try to solve the problem of emergency backup in inter-DC networks with progressive disasters. We first utilize the time-expanded network (TEN) approach to model the time-variant inter-DC network during a progressive disaster as a variant TEN (VTEN) and convert the dynamic flow scheduling for emergency backup to a static one. Then, with the VTEN, we formulate an optimization model to maximize the profit from the emergency backup in consideration of data values and resource costs. Although this large-scale optimization can be solved in a distributed way by leveraging the alternation direction method of multipliers (ADMM), we find that one of its subproblems is nontrivial in the distributed setting. We propose a novel inexact ADMM approach to resolve the issue induced by the subproblem, and prove that the proposed algorithm can converge to the optimal solution. The results from extensive simulations confirm that our algorithm is robust and time-efficient, and outperforms several benchmarks in terms of backup profit and running time.

This paper considers the emergency backup in an inter-datacenter (inter-DC) network whose topology is time-variant due to the progress of a disaster. We first transform the dynamic backup into a static flow problem through building a variable time-expanded network (V-TEN). Then, by considering both data utility and resource cost, we formulate an optimization to maximize the backup profit and leverage the alternating direction method of multipliers (ADMM) to design a time-efficient and distributed algorithm. Simulation results show that our ADMM-based algorithm outperforms several existing ones.

Image blurring significantly degrades the image recognition performance. In this paper, we novelly address the blurred image recognition task from the perspective of domain adaptation (DA). The scenario is that, the training set (source domain) only comprises of the labelled clear images, and the test set (target domain) is composed of the unlabelled blurred images. DA is executed to eliminate the domain shift by subspace alignment. In this way, the clear and blurred image domains are pushed closer in the feature space. The supervised LMDR metric learning method is employed by us to construct the source domain subspace for further performance enhancement, compared to the unsupervised one (i.e., PCA). The experimental results on two datasets demonstrate that, the proposed DA-based blurred image recognition mechanism can significantly enhance the performance of different kinds of visual descriptors, especially when the blurring degree is strong.

Blurred image recognition is still remaining as a challenging task, while with the wide applications. One principal way for solving this problem is to extract the blur-invariant visual descriptor. To this end, local phase quantization (LPQ) was ever proposed, and achieved promising results. In this paper, to further enhance LPQ's performance, we propose to apply Fisher Vector (FV) encoding approach to acquire the mid-level blurred image representation. To our knowledge, it is the first time that the descriptive power of FV for blurred image recognition has been investigated. Instead of being extracted holistically from the whole image as previously, LPQ is acquired in a densely sampled way. That is, a sliding sub-window will screen the image with certain vertical and horizontal strides. LPQs are then extracted from all the resulting sub-windows respectively. In addition, to maintain local spatial structure information, each sub-window will be divided into finer cells. After being FV encoded, the local LPQs are aggregated using sum-pooling to generate the image signature. The experimental results on three datasets demonstrate that FV can enhance LPQ's performance significantly, and our proposition also outperforms the other blur-invariant descriptors by large margins in most cases.