Trusted resource allocation in volunteer edge-cloud computing for scientific applications

Data-intensive science applications in fields such as e.g., bioinformatics, health sciences, and material discovery are becoming increasingly dynamic and demanding with resource requirements. Researchers using these applications which are based on advanced scientific workflows frequently require a diverse set of resources that are often not available within private servers or a single Cloud Service Provider (CSP). For example, a user working with Precision Medicine applications would prefer only those CSPs who follow guidelines from HIPAA (Health Insurance Portability and Accountability Act) for implementing their data services and might want services from other CSPs for economic viability. With the generation of more and more data these workflows often require deployment and dynamic scaling of multi-cloud resources in an efficient and high-performance manner (e.g., quick setup, reduced computation time, and increased application throughput). At the same time, users seek to minimize the costs of configuring the related multi-cloud resources. While performance and cost are among the key factors to decide upon CSP resource selection, the scientific workflows often process proprietary/confidential data that introduces additional constraints of security postures. Thus, users have to make an informed decision on the selection of resources that are most suited for their applications while trading off between the key factors of resource selection which are performance, agility, cost, and security (PACS). Furthermore, even with the most efficient resource allocation across multi-cloud, the cost to solution might not be economical for all users which have led to the development of new paradigms of computing such as volunteer computing where users utilize volunteered cyber resources to meet their computing requirements. For economical and readily available resources, it is essential that such volunteered resources can integrate well with cloud resources for providing the most efficient computing infrastructure for users. In this dissertation, individual stages such as user requirement collection, user's resource preferences, resource brokering and task scheduling, in lifecycle of resource brokering for users are tackled. For collection of user requirements, a novel approach through an iterative design interface is proposed. In addition, fuzzy interference-based approach is proposed to capture users' biases and expertise for guiding their resource selection for their applications. The results showed improvement in performance i.e. time to execute in 98 percent of the studied applications. The data collected on user's requirements and preferences is later used by optimizer engine and machine learning algorithms for resource brokering. For resource brokering, a new integer linear programming based solution (OnTimeURB) is proposed which creates multi-cloud template solutions for resource allocation while also optimizing performance, agility, cost, and security. The solution was further improved by the addition of a machine learning model based on naive bayes classifier which captures the true QoS of cloud resources for guiding template solution creation. The proposed solution was able to improve the time to execute for as much as 96 percent of the largest applications. As discussed above, to fulfill necessity of economical computing resources, a new paradigm of computing viz-a-viz Volunteer Edge Computing (VEC) is proposed which reduces cost and improves performance and security by creating edge clusters comprising of volunteered computing resources close to users. The initial results have shown improved time of execution for application workflows against state-of-the-art solutions while utilizing only the most secure VEC resources. Consequently, we have utilized reinforcement learning based solutions to characterize volunteered resources for their availability and flexibility towards implementation of security policies. The characterization of volunteered resources facilitates efficient allocation of resources and scheduling of workflows tasks which improves performance and throughput of workflow executions. VEC architecture is further validated with state-of-the-art bioinformatics workflows and manufacturing workflows.Includes bibliographical references

Open Research Knowledge Graph

As we mark the fifth anniversary of the alpha release of the Open Research Knowledge Graph (ORKG), it is both timely and exhilarating to celebrate the significant strides made in this pioneering project. We designed this book as a tribute to the evolution and achievements of the ORKG and as a practical guide encapsulating its essence in a form that resonates with both the general reader and the specialist. The ORKG has opened a new era in the way scholarly knowledge is curated, managed, and disseminated. By transforming vast arrays of unstructured narrative text into structured, machine-processable knowledge, the ORKG has emerged as an essential service with sophisticated functionalities. Over the past five years, our team has developed the ORKG into a vibrant platform that enhances the accessibility and visibility of scientific research. This book serves as a non-technical guide and a comprehensive reference for new and existing users that outlines the ORKG’s approach, technologies, and its role in revolutionizing scholarly communication. By elucidating how the ORKG facilitates the collection, enhancement, and sharing of knowledge, we invite readers to appreciate the value and potential of this groundbreaking digital tool presented in a tangible form. Looking ahead, we are thrilled to announce the upcoming unveiling of promising new features and tools at the fifth-year celebration of the ORKG’s alpha release. These innovations are set to redefine the boundaries of machine assistance enabled by research knowledge graphs. Among these enhancements, you can expect more intuitive interfaces that simplify the user experience, and enhanced machine learning models that improve the automation and accuracy of data curation. We also included a glossary tailored to clarifying key terms and concepts associated with the ORKG to ensure that all readers, regardless of their technical background, can fully engage with and understand the content presented. This book transcends the boundaries of a typical technical report. We crafted this as an inspiration for future applications, a testament to the ongoing evolution in scholarly communication that invites further collaboration and innovation. Let this book serve as both your guide and invitation to explore the ORKG as it continues to grow and shape the landscape of scientific inquiry and communication

Conceptual development of custom, domain-specific mashup platforms

Despite the common claim by mashup platforms that they enable end-users to develop their own software, in practice end-users still don't develop their own mashups, as the highly technical or inexistent user bases of today's mashup platforms testify. The key shortcoming of current platforms is their general-purpose nature, that privileges expressive power over intuitiveness. In our prior work, we have demonstrated that a domainspecific mashup approach, which privileges intuitiveness over expressive power, has much more potential to enable end-user development (EUD). The problem is that developing mashup platforms - domain-specific or not - is complex and time consuming. In addition, domain-specific mashup platforms by their very nature target only a small user basis, that is, the experts of the target domain, which makes their development not sustainable if it is not adequately supported and automated. With this article, we aim to make the development of custom, domain-specific mashup platforms costeffective. We describe a mashup tool development kit (MDK) that is able to automatically generate a mashup platform (comprising custom mashup and component description languages and design-time and runtime environments) from a conceptual design and to provision it as a service. We equip the kit with a dedicated development methodology and demonstrate the applicability and viability of the approach with the help of two case studies. © 2014 ACM

Approaches to visualising linked data: a survey

The uptake and consumption of Linked Data is currently restricted almost entirely to the Semantic Web community. While the utility of Linked Data to non-tech savvy web users is evident, the lack of technical knowledge and an understanding of the intricacies of the semantic technology stack limit such users in their ability to interpret and make use of the Web of Data. A key solution in overcoming this hurdle is to visualise Linked Data in a coherent and legible manner, allowing non-domain and non-technical audiences to obtain a good understanding of its structure, and therefore implicitly compose queries, identify links between resources and intuitively discover new pieces of information. In this paper we describe key requirements which the visualisation of Linked Data must fulfil in order to lower the technical barrier and make the Web of Data accessible for all. We provide an extensive survey of current efforts in the Semantic Web community with respect to our requirements, and identify the potential for visual support to lead to more effective, intuitive interaction of the end user with Linked Data. We conclude with the conclusions drawn from our survey and analysis, and present proposals for advancing current Linked Data visualisation efforts

The Evolution of myExperiment

The myExperiment social website for sharing scientific workflows, designed according to Web 2.0 principles, has grown to be the largest public repository of its kind. It is distinctive for its focus on sharing methods, its researcher-centric design and its facility to aggregate content into sharable 'research objects'. This evolution of myExperiment has occurred hand in hand with its users. myExperiment now supports Linked Data as a step toward our vision of the future research environment, which we categorise here as '3rd generation e-Research'

The Innovation-to-Occupations Ontology: Linking Business Transformation Initiatives to Occupations and Skills

The fast adoption of new technologies forces companies to continuously adapt their operations making it harder to predict workforce requirements. Several recent studies have attempted to predict the emergence of new roles and skills in the labour market from online job ads. This paper aims to present a novel ontology linking business transformation initiatives to occupations and an approach to automatically populating it by leveraging embeddings extracted from job ads and Wikipedia pages on business transformation and emerging technologies topics. To our knowledge, no previous research explicitly links business transformation initiatives, like the adoption of new technologies or the entry into new markets, to the roles needed. Our approach successfully matches occupations to transformation initiatives under ten different scenarios, five linked to technology adoption and five related to business. This framework presents an innovative approach to guide enterprises and educational institutions on the workforce requirements for specific business transformation initiatives.Comment: 14 pages, 3 figures, Camera-ready version in ACIS 202

Evaluating FAIR Digital Object and Linked Data as distributed object systems

FAIR Digital Object (FDO) is an emerging concept that is highlighted by European Open Science Cloud (EOSC) as a potential candidate for building a ecosystem of machine-actionable research outputs. In this work we systematically evaluate FDO and its implementations as a global distributed object system, by using five different conceptual frameworks that cover interoperability, middleware, FAIR principles, EOSC requirements and FDO guidelines themself. We compare the FDO approach with established Linked Data practices and the existing Web architecture, and provide a brief history of the Semantic Web while discussing why these technologies may have been difficult to adopt for FDO purposes. We conclude with recommendations for both Linked Data and FDO communities to further their adaptation and alignment.Comment: 40 pages, submitted to PeerJ C

Unified Management of Applications on Heterogeneous Clouds

La diversidad con la que los proveedores cloud ofrecen sus servicios, definiendo sus propias interfaces y acuerdos de calidad y de uso, dificulta la portabilidad y la interoperabilidad entre proveedores, lo que incurre en el problema conocido como el bloqueo del vendedor. Dada la heterogeneidad que existe entre los distintos niveles de abstracción del cloud, como IaaS y PaaS, hace que desarrollar aplicaciones agnósticas que sean independientes de los proveedores y los servicios en los que se van a desplegar sea aún un desafío. Esto también limita la posibilidad de migrar los componentes de aplicaciones cloud en ejecución a nuevos proveedores. Esta falta de homogeneidad también dificulta el desarrollo de procesos para operar las aplicaciones que sean robustos ante los errores que pueden ocurrir en los distintos proveedores y niveles de abstracción. Como resultado, las aplicaciones pueden quedar ligadas a los proveedores para las que fueron diseñadas, limitando la capacidad de los desarrolladores para reaccionar ante cambios en los proveedores o en las propias aplicaciones. En esta tesis se define trans-cloud como una nueva dimensión que unifica la gestión de distintos proveedores y niveles de servicios, IaaS y PaaS, bajo una misma API y hace uso del estándar TOSCA para describir aplicaciones agnósticas y portables, teniendo procesos automatizados, por ejemplo para el despliegue. Por otro lado, haciendo uso de las topologías estructuradas de TOSCA, trans-cloud propone un algoritmo genérico para la migración de componentes de aplicaciones en ejecución. Además, trans-cloud unifica la gestión de los errores, permitiendo tener procesos robustos y agnósticos para gestionar el ciclo de vida de las aplicaciones, independientemente de los proveedores y niveles de servicio donde se estén ejecutando. Por último, se presentan los casos de uso y los resultados de los experimentos usados para validar cada una de estas propuestas

Recognition of resource patterns in human-centric processes : a case study

Business experts need to improve business processes by increasing process efficiency and reducing process costs. To achieve this, a common method is to capture a series of repeatedly conducted process activities and their structure, i.e. the business logic of the process, and then enact process execution based on it. However, there exist informal processes, which are human-centric processes that are highly dynamic. Since this approach assumes the existence of predictable business logic of the process, it does not apply for management of informal processes. The Informal Process Essentials (IPE) model is a modeling approach for informal processes. This model depicts informal processes by documenting resources used in these process. Through this approach, we are able to retain best practice and knowledge accumulated in the processes. Based on this approach, there is also the InProXec method to enable the application of the IPE approach in organizations. In this thesis work, we want to validate the concepts introduced in the InProXec method using a case study on the jclouds project. To achieve this aim, we introduce the concept of a generic mapping mechanism and an evolving correlation coefficient function. Based on these concepts, we present the Informal Process Discoverer (IPD) service. The IPD service is an implementation of the discovery of IPE models. The test results of the IPD service have shown that the service is successful in discovering the IPE model and giving process recommendations. For example, using an informal process model with includes 7 human resources and 2 GitHub repositories as input, we are able to discover 74 other resources that participate in the process including 65 human resources and 9 Git repositories