Shuai Zheng

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—While early emphasis of Infrastructure as a Service (IaaS) clouds was on providing resource elasticity to end users, providers are increasingly interested in over-committing their resources to maximize the utilization and returns of their capital investments. In principle, over-committing resources hedges that users—on average—only need a small portion of(More)
Real life data often includes information from different channels. For example, in computer vision, we can describe an image using different image features, such as pixel intensity, color, HOG, GIST feature, SIFT features , etc.. These different aspects of the same objects are often called multi-view (or multi-modal) data. Low-rank regression model has been(More)
—Intrinsic to " big data " processing workloads (e.g., iterative MapReduce, Pregel, etc.) are cyclical resource utilization patterns that are highly synchronized across different resource types as well as the workers in a cluster. In Infrastructure as a Service settings, cloud providers do not exploit this characteristic to better manage VMs because they(More)
Social influence among users (e.g., collaboration on a project) creates bursty behavior in the underlying high performance computing (HPC) workloads. Using representative HPC and cluster workload logs, this paper identifies, analyzes, and quantifies the level of social influence across HPC users. We show the existence of a social graph that is characterized(More)
Deep Learning is a very powerful machine learning model. Deep Learning trains a large number of parameters for multiple layers and is very slow when data is in large scale and the architecture size is large. Inspired from the shrinking technique used in accelerating computation of Support Vector Machines (SVM) algorithm and screening technique used in(More)
—Linear Discriminant Analysis (LDA) is a widely-used supervised dimensionality reduction method in computer vision and pattern recognition. In null space based LDA (NLDA), a well-known LDA extension, between-class distance is maximized in the null space of the within-class scatter matrix. However, there are some limitations in NLDA. Firstly, for many data(More)
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