Dmitry Duplyakin

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With the proliferation of infrastructure clouds it is now possible to consider developing applications capable of leveraging multi-cloud environments. Such environments provide users a unique opportunity to tune their deployments to meet specific needs (e.g., cost, reliability, performance, etc.). Open source multi-cloud scaling tools, such as Nimbus(More)
Active Learning (AL) is a methodology from machine learning in which the learner interacts with the data source. In this paper, we investigate application of AL techniques to a new domain: regression problems in performance analysis. For computational systems with many factors, each of which can take on many levels, fixed experiment designs can require many(More)
Users of CloudLab (and other GENI-derived testbeds) commonly use image snapshots to preserve their working environments and to share them with other users. While snapshots re-create software environments byte-for-byte, they are not conducive to composing multiple environments, nor are they good for experiments that must run across many versions of their(More)
We present an architecture that increases persistence and reliability of automated infrastructure management in the context of hybrid, cluster-cloud environments. We describe our highly available implementation that builds upon Chef configuration management system and infrastructure-as-a-service cloud resources from Amazon Web Services. We summarize our(More)
Streamlined configuration management plays a significant role in modern, complex distributed systems. Via mechanisms that promote consistency, repeatability, and transparency, configuration management systems (CMSes) address complexity and aim to increase the efficiency of administrative procedures, including deployment and failure recovery scenarios.(More)
Clouds, HPC clusters, HTC systems, and testbeds all serve different parts of the computing ecosystem: each are designed for different types of workloads and suited to different types of research and commercial users. We propose that an effective way to share resources among these diverse applications is to not shoehorn them all into the same resource(More)
Load balancing and partitioning are critical when it comes to parallel computations. Popular partitioning strategies based on space filling curves focus on equally dividing work. The partitions produced are independent of the architecture or the application. Given the ever-increasing relative cost of data movement and increasing heterogeneity of our(More)
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