Related papers: Scientific Workflow Repeatability through Cloud-Aw…
Demand is growing for more accountability regarding the technological systems that increasingly occupy our world. However, the complexity of many of these systems - often systems-of-systems - poses accountability challenges. A key reason…
Scientific workflows are a cornerstone of modern scientific computing, and they have underpinned some of the most significant discoveries of the last decade. Many of these workflows have high computational, storage, and/or communication…
Scientists rely on simulations to study natural phenomena. Trusting the simulation results is vital to develop sciences in any field. One approach to build trust is to ensure the reproducibility and traceability of the simulations through…
Data provenance, or data lineage, describes the life cycle of data. In scientific workflows on HPC systems, scientists often seek diverse provenance (e.g., origins of data products, usage patterns of datasets). Unfortunately, existing…
Scientific workflows are widely used to automate scientific data analysis and often involve processing large quantities of data on compute clusters. As such, their execution tends to be long-running and resource intensive, leading to…
We outline the approach being developed in the neuGRID project to use provenance management techniques for the purposes of capturing and preserving the provenance data that emerges in the specification and execution of workflows in…
We outline the approach being developed in the neuGRID project to use provenance management techniques for the purposes of capturing and preserving the provenance data that emerges in the specification and execution of workflows in…
We propose an approach for improved reproducibility that includes capturing and relating provenance characteristics and performance metrics, in a hybrid queriable system, the ProvEn server. The system capabilities are illustrated on two use…
Today, the number of data-intensive and compute-intensive applications like business and scientific workflows has dramatically increased, which made cloud computing more popular in the matter of delivering a large amount of computing…
Scientific knowledge increasingly depends on complex computational processes where both hardware and software layers can influence research outcomes. As computational complexity grows, classical-quantum integration provides a lens for…
Cloud-native is an approach to building and running scalable applications in modern cloud infrastructures, with the Kubernetes container orchestration platform being often considered as a fundamental cloud-native building block. In this…
Infrastructure as a service clouds hide the complexity of maintaining the physical infrastructure with a slight disadvantage: they also hide their internal working details. Should users need knowledge about these details e.g., to increase…
Grid Computing is a type of parallel and distributed systems that is designed to provide reliable access to data and computational resources in wide area networks. These resources are distributed in different geographical locations, however…
In a new effort to make our research transparent and reproducible by others, we developed a workflow to run and share computational studies on the public cloud Microsoft Azure. It uses Docker containers to create an image of the application…
In an organization specifically as virtual as cloud there is need for access control systems to constrain users direct or backhanded action that could lead to breach of security. In cloud, apart from owner access to confidential data the…
Failure is inevitable in scientific computing. As scientific applications and facilities increase their scales over the last decades, finding the root cause of a failure can be very complex or at times nearly impossible. Different…
Archival research is a complicated task that involves several diverse activities for the extraction of evidence and knowledge from a set of archival documents. The involved activities are usually unconnected, in terms of data connection and…
The reproducibility of scientific experiment is vital for the advancement of disciplines based on previous work. To achieve this goal, many researchers focus on complex methodology and self-invented tools which have difficulty in practical…
Scientific workflow is a powerful tool to streamline and organize computational steps of scientific application. This paper presents Emerald, a system that adds sophisticated cloud offloading capabilities to scientific workflows. Emerald…
Scientific workflows have become integral tools in broad scientific computing use cases. Science discovery is increasingly dependent on workflows to orchestrate large and complex scientific experiments that range from execution of a…