Docker offers an opportunity for further improvement in data centers' (DCs) efficiency. However, existing models and schemes fall short to be efficiently used for Docker container-based resource allocation. We design a novel application oriented Docker container (AODC)-based resource allocation framework to minimize the application deployment cost in DCs, and to support automatic scaling while the workload of cloud applications varies. We then model the AODC resource allocation problem considering features of Docker, various applications' requirements, and available resources in cloud data centers, and propose a scalable algorithm for DCs with diverse and dynamic applications and massive physical resources.

Clustering organizes nodes into groups in order to enhance the connectivity and stability of cognitive radio sensor networks. Mainly depending on the channel availability, many existing spectrum-aware clustering algorithms may not achieve the most satisfactory clustering. Taking into account the various influence factors to establish the optimal clustering is a challenge to enhance the network performance. This paper proposes a novel spectrum-aware clustering algorithm based on weighted clustering metric to obtain the optimal clustering by solving an optimization model. The new weighted clustering metric, simultaneously evaluating temporal-spatial correlation, confidence level and residual energy, is used to elect clusterheads and ally member nodes. After clustering, the clusterheads sensing spectrum instead of all member nodes greatly reduces the energy consumption of spectrum sensing and increases the opportunity of data transmission. The performance comparison between the traditional spectrum-aware clustering algorithms and our proposed algorithm has been highlighted with the experiments.

Increasing evidence shows that autophagy impairment is involved in the pathogenesis and progression of neurodegenerative diseases including Parkinson’s disease (PD). We previously identified a natural alkaloid named corynoxine B (Cory B) as a neuronal autophagy inducer. However, its brain permeability is relatively low, which hinders its potential use in treating PD. Thus we synthesized various derivatives of Cory B to find more potent autophagy inducers with improved brain bioavailability. In this study, we evaluated the autophagy-enhancing effect of CB6 derivative and its neuroprotective action against PD in vitro and in vivo. We showed that CB6 (5–40 μM) dose-dependently accelerated autophagy flux in cultured N2a neural cells through activating the PIK3C3 complex and promoting PI3P production. In MPP⁺-treated PC12 cells, CB6 inhibited cell apoptosis and increased cell viability by inducing autophagy. In MPTP-induced mouse model of PD, oral administration of CB6 (10, 20 mg· kg⁻¹_· d⁻¹, for 21 days) significantly improved motor dysfunction and prevented the loss of dopaminergic neurons in the striatum and substantia nigra pars compacta. Collectively, compound CB6 is a brain-permeable autophagy enhancer via PIK3C3 complex activation, which may help the prevention or treatment of PD.

Application offloading plays a crucial role in application deployment in edge-cloud computing. However, finding the optimal solution for application offloading is challenging due to the heterogeneous resources, computing dependency of tasks, and complex network. Existing research on application offloading problems often assumes that the communication delay between the edge and the cloud (inter-side) is symmetrical or that within the cloud or edge (intra-side) can be omitted. However, this assumption is not practical considering the distinct features of the clouds and the edge clusters. Therefore, we study application offloading in the heterogeneous edge-cloud environment by considering both intra-side communication delay between tasks assigned to the same side and asymmetry inter-side communication delay between edge and cloud sides. We first focus on the specific circumstances with a boundary condition that lead to an optimal offloading solution in a heterogeneous edge-cloud environment. Then we study the general case by designing an iterative algorithm with maximum gain technique to solve it. Furthermore, considering various bandwidths within each side and resource capacities of physical nodes, we develop two efficient algorithms by combining minimum cut and maximum gain approaches. Both simulations and real trace-based evaluations are conducted to validate that the proposed algorithms outperform existing solutions.

Along with the rapid proliferation of mobile applications, Mobile Edge Computing (MEC) attracts growing interests in recent years. The number of service handovers due to users' movements between different MEC regions impacts heavily on QoE and service operation cost. In this poster, we propose a randomized algorithm to minimize the number of possible handovers between different MEC regions by carefully dividing a metropolitan area into disjoint clusters. Preliminary experiments exhibit that the proposed algorithm could find sub-optimal partitions with reduced total number of handovers.

Crowd counting is still a challenging task due to the variability of the distance scale, crowd occlusion, and complex background information. However, the deep convolution neural network has been proved to be effective in solving these problems. By loading input images, the network generates predicted density maps, and the average absolute error between the predicted density maps and given ground truth (GT) maps is a solid standard for evaluating the quality of the network. We propose a mask-based generative adversarial network (MBGAN) structure to generate accurate predicted density maps. The network consists of two parts: the generator and the discriminator. In the generator, we embed a fundamental feature extracting module, multiple level dilated convolution blocks, a predicted mask, and shortcut connection operations. The discriminator is mainly used to distinguish whether the density map comes from the generator or the GT and urges the generator to produce the density map that can confuse itself. The training of the proposed MBGAN model is through the joint action of density loss and adversarial loss. In the training strategy, we use the cross training of the generator and discriminator. Through experiments on five available datasets, the MBGAN achieved state-of-the-art performances that outperform other advanced methods.