Analyzing Data-center Application Performance Via Constraint-based Models

Hyperscale Data Centers (HDCs) are the largest distributed computing machines ever constructed. They serve as the backbone for many popular applications, such as YouTube, Netflix, Meta, and Airbnb, which involve millions of users and generate billions in revenue. As the networking infrastructure plays a pivotal role in determining the performance of HDC applications, understanding and optimizing their networking performance is critical. This thesis proposes and evaluates a constraint-based approach to characterize the networking performance of HDC applications. Through extensive evaluations conducted in both controlled settings and real-world case studies within a production HDC, I demonstrated the effectiveness of the constraint-based approach in handling the immense volume of performance data in HDCs, achieving tremendous dimension reduction, and providing very useful interpretability.Doctor of Philosoph

No-reference image quality assessment based on the AdaBoost BP neural network in the wavelet domain

Considering the relatively poor robustness of quality scores for different types of distortion and the lack of mechanism for determining distortion types, a no-reference image quality assessment (NR-IQA) method based on the AdaBoost BP Neural Network in Wavelet domain (WABNN) is proposed. A 36-dimensional image feature vector is constructed by extracting natural scene statistics (NSS) features and local information entropy features of the distorted image wavelet sub-band coefficients in three scales. The ABNN classifier is obtained by learning the relationship between image features and distortion types. The ABNN scorer is obtained by learning the relationship between image features and image quality scores. A series of contrast experiments are carried out in the LIVE database and TID2013 database. Experimental results show the high accuracy of the distinguishing distortion type, the high consistency with subjective scores and the high robustness of the method for distorted images. Experiment results also show the independence for the database and the relatively high operation efficiency of this method

A full-reference image quality assessment for multiply distorted image based on visual mutual information

A full-reference image quality assessment (FR-IQA) method for multi-distortion based on visual mutual information (MD-IQA) is proposed to solve the problem that the existing FR-IQA methods are mostly applicable to single-distorted images, but the assessment result for multiply distorted images is not ideal. First, the reference image and the distorted image are preprocessed by steerable pyramid decomposition and contrast sensitivity function (CSF). Next, a Gaussian scale mixture (GSM) model and an image distorted model are respectively constructed for the reference images and the distorted images. Then, visual distorted models are constructed both for the reference images and the distorted images. Finally, the mutual information between the processed reference image and the distorted image is calculated to obtain the full-reference quality assessment index for multiply distorted images. The experimental results show that the proposed method has higher accuracy and better performance for multiply distorted images

Real time unmanned aerial vehicle tracking of fast moving small target on ground

In order to solve problems of occlusion and fast motion of small targets in UAV (Unmanned Aerial Vehicle) target tracking , an adaptive algorithm which fuses the improved color histogram tracking response and the correlation filter tracking response based on multi-channel HOG features is proposed to realize small target tracking with high accuracy. The state judgment index is used to determine whether the target is in a fast motion or an occlusion state. In the fast motion state, the search area is enlarged, and the color optimal model which suppresses the suspected area is used for rough detection. Then, re-detection in the location of multiple peaks in the rough detection response is carried out using the correlation filter to accurately locate the target. In an occlusion state, the model stops updating, the search area is expanded, and the current color model is used for rough detection. Then, re-detection in the place of multiple peaks in the rough detection response is carried out using the correlation filter to accurately locate the target. Experimental results show that the proposed method can track small targets accurately. The frame rate of the proposed method is 40.23 frames/second, indicating usable real-time performance

PAHs in the North Atlantic Ocean and the Arctic Ocean: Spatial Distribution and Water Mass Transport

In the Arctic Ocean, it is still unclear what role oceanic transport plays in the fate of semivolatile organic compounds. The strong-stratified Arctic Ocean undergoes complex inputs and outputs of polycyclic aromatic hydrocarbons (PAHs) from the neighboring oceans and continents. To better understand PAHs’ transport processes and their contribution to high-latitude oceans, surface seawater, and water column, samples were collected from the North Atlantic Ocean and the Arctic Ocean in 2012. The spatial distribution of dissolved PAHs (∑9PAH) in surface seawater showed an “Arctic Shelf \u3e Atlantic Ocean \u3e Arctic Basin” pattern, with a range of 0.3–10.2 ng L−1. Positive matrix factorization modeling results suggested that vehicle emissions and biomass combustion were the major PAHs sources in the surface seawater. According to principal component analysis, PAHs in different water masses showed unique profiles indicating their different origins. Carried by the Norwegian Atlantic Current (0–800 m) and East Greenland Current (0–300 m), PAH individuals’ net transport mass fluxes ranged from −4.4 ± 1.7 to 53 ± 39 tons year−1 to the Arctic Ocean. We suggested the limited contribution of ocean currents on PAHs’ delivery to the Arctic Ocean, but their role in modulating PAHs’ air–sea interactions and other biogeochemical processes needs further studies

Storax Protected Oxygen-Glucose Deprivation/Reoxygenation Induced Primary Astrocyte Injury by Inhibiting NF-κB Activation in vitro

Stroke is the second leading cause of death and the leading cause of long-term disability in the world. There is an urgent unmet need to develop a range of neuroprotective strategies to restrain the damage that occurs in the hours and days following a stroke. Storax, a natural resin extracted from injuring Liquidambar orientalis Mill, has been used to treat acute stroke in traditional Chinese medicine for many centuries. Storax has demonstrated the neuroprotective effects in cerebrovascular diseases. However, the neuroprotective mechanisms activated by storax in ischemia/reperfusion-injured astrocytes have not been elucidated. In this study, we established an oxygen-glucose deprivation/reoxygenation (OGD/R)-induced astrocytes injury model to investigate the effects of storax on OGD/R-induced astrocytes injury and potential mechanisms. Experimental results showed that storax alleviated expression of inflammatory cytokines and protected primary cortical astrocytes injured by OGD/R. Furthermore, storax could inhibit NF-κB activation in injured astrocytes by OGD/R and inhibition of NF-κB with Bay-11-7082 obscured the neuroprotective effects of storax. In conclusion, storax alleviated expression of inflammatory cytokines and protected primary cortical astrocytes injured by OGD/R, which was partially mediated by NF-κB signaling pathway activation