In this paper, the initial mechanisms of nanoparticle formation and growth in radiofrequency acetylene (C2H2) plasmas are investigated by means of a comprehensive self-consistent one-dimensional (1D) fluid model. This model is an extension of the 1D fluid model, developed earlier by De Bleecker et al. Based on the comparison of our previous results with available experimental data for acetylene plasmas in the literature, some new mechanisms for negative ion formation and growth are proposed. Possible routes are considered for the formation of larger (linear and branched) hydrocarbons C2nH2 (n = 3, 4, 5), which contribute to the generation of C2nH− anions (n = 3, 4, 5) due to dissociative electron attachment. Moreover, the vinylidene anion (H2CC−) and higher
anions (n = 2–4) are found to be important plasma species.
Abstract-Clouds have become an attractive computing platform which offers on-demand computing power and storage capacity. Its dynamic scalability enables users to quickly scale up and scale down underlying infrastructure in response to business volume, performance desire and other dynamic behaviors. However, challenges arise when considering computing instance non-deterministic acquisition time, multiple VM instance types, unique cloud billing models and user budget constraints. Planning enough computing resources for user desired performance with less cost, which can also automatically adapt to workload changes, is not a trivial problem. In this paper, we present a cloud auto-scaling mechanism to automatically scale computing instances based on workload information and performance desire. Our mechanism schedules VM instance startup and shut-down activities. It enables cloud applications to finish submitted jobs within the deadline by controlling underlying instance numbers and reduces user cost by choosing appropriate instance types. We have implemented our mechanism in Windows Azure platform, and evaluated it using both simulations and a real scientific cloud application. Results show that our cloud auto-scaling mechanism can meet user specified performance goal with less cost.
A significant open issue in cloud computing is performance. Few, if any, cloud providers or technologies offer quantitative performance guarantees. Regardless of the potential advantages of the cloud in comparison to enterprise-deployed applications, cloud infrastructures may ultimately fail if deployed applications cannot predictably meet behavioral requirements. In this paper, we present the results of comprehensive performance experiments we conducted on Windows Azure from October 2009 to February 2010. In general, we have observed good performance of the Windows Azure mechanisms, although the average 10 minute VM startup time and the worst-case 2x slowdown for SQL Azure in certain situations (relative to commodity hardware within the enterprise) must be accounted for in application design. In addition to a detailed performance evaluation of Windows Azure, we provide recommendations for potential users of Windows Azure based on these early observations. Although the discussion and analysis is tailored to scientific applications, the results are broadly applicable to the range of existing and future applications running in Windows Azure.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.