Learn More
Cloud computing has emerged as a new approach to large scale computing and is attracting a lot of attention from the scientific and research computing communities. Despite its growing popularity, it is still unclear just how well the cloud model of computation will serve scientific applications. In this paper we analyze the applicability of cloud to the(More)
Microsoft robotics studio (MSRS) was publicly released in December 2006 with the explicit goal of providing an industry software standard for robot control. To become a viable standard, several technical challenges needed to be solved. In this article, we examine the composition of MSRS, looking generally at its architecture and specifically at its(More)
To effectively support real-time monitoring and performance analysis of scientific workflow execution, varying levels of event data must be captured and made available to interested parties. This paper discusses the creation of an ontology-aware workflow monitoring system for use in the Trident system which utilizes a distributed publish/subscribe event(More)
Leveraging cloud computing to acquire the necessary computation resources to scale out parallel applications is becoming common practice. However, many such applications also require communication and synchronization between processes. Although, commercial cloud platforms provide ready access to scalable compute and storage services, implementing(More)
Carrying out science at extreme scale is the next generational challenge facing the broad field of scientific research. Cloud computing offers to potential for an increasing number of researchers to have ready access to the large scale compute resources required to tackle new challenges in their field. Unfortunately barriers of complexity remain for(More)
As the emergence of cloud computing brings the potential for large-scale data analysis to a broader community, architectural patterns for data analysis on the cloud, especially those addressing iterative algorithms, are increasingly useful. MapReduce suffers performance limitations for this purpose as it is not inherently designed for iterative algorithms.(More)