GreenLight Data Collection Architecture

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The document discusses initial steps in creating an energy model for the BlackBox cyberinfrastructure project. It identifies objectives such as understanding energy consumption during task execution, optimizing scheduling to reduce environmental impact, and providing different computation modes to users. Next steps include defining models for green data and GreenLight resources, identifying efficient scheduling policies, and creating services to manage and control the BlackBox.

Technology

Data Collection meeting Claudiu Farcas, Filippo Seracini, Ingolf Krueger

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Data Mining the domain of our project , is a newly developed sub-field of computer science engineering , it is the analysis step of Knowledge discovery in databases(KDD ) process and is used for extraction of data from a huge data set and make it understandable for further use. Among the Six classes of data mining our choice of interest and our project area is the Association Rule Mining. We will be applying this class of data mining in an efficient and frequent pattern for the mining of knowledge or data from Distributed System , which can be explained as a collection of set of computers that act , work and appear as one large computer.

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The title of this talk is a crass attempt to be catchy and topical, by referring to the recent victory of Watson in Jeopardy. My point (perhaps confusingly) is not that new computer capabilities are a bad thing. On the contrary, these capabilities represent a tremendous opportunity for science. The challenge that I speak to is how we leverage these capabilities without computers and computation overwhelming the research community in terms of both human and financial resources. The solution, I suggest, is to get computation out of the lab—to outsource it to third party providers. Abstract follows: We have made much progress over the past decade toward effective distributed cyberinfrastructure. In big-science fields such as high energy physics, astronomy, and climate, thousands benefit daily from tools that enable the distributed management and analysis of vast quantities of data. But we now face a far greater challenge. Exploding data volumes and new research methodologies mean that many more--ultimately most?--researchers will soon require similar capabilities. How can we possible supply information technology (IT) at this scale, given constrained budgets? Must every lab become filled with computers, and every researcher an IT specialist? I propose that the answer is to take a leaf from industry, which is slashing both the costs and complexity of consumer and business IT by moving it out of homes and offices to so-called cloud providers. I suggest that by similarly moving research IT out of the lab, we can realize comparable economies of scale and reductions in complexity, empowering investigators with new capabilities and freeing them to focus on their research. I describe work we are doing to realize this approach, focusing initially on research data lifecycle management. I present promising results obtained to date, and suggest a path towards large-scale delivery of these capabilities. I also suggest that these developments are part of a larger "revolution in scientific affairs," as profound in its implications as the much-discussed "revolution in military affairs" resulting from more capable, low-cost IT. I conclude with some thoughts on how researchers, educators, and institutions may want to prepare for this revolution.

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