Enabling Scientific Workflow Sharing through Coarse-Grained Interoperability

E-scientists want to run their scientific experiments on Distributed Computing Infrastructures (DCI) to be able to access large pools of resources and services. To run experiments on these infrastructures requires specific expertise that e-scientists may not have. Workflows can hide resources and services as a virtualization layer providing a user interface that e-scientists can use. There are many workflow systems used by research communities but they are not interoperable. To learn a workflow system and create workflows in this workflow system may require significant efforts from e-scientists. Considering these efforts it is not reasonable to expect that research communities will learn new workflow systems if they want to run workflows developed in other workflow systems. The solution is to create workflow interoperability solutions to allow workflow sharing. The FP7 Sharing Interoperable Workflow for Large-Scale Scientific Simulation on Available DCIs (SHIWA) project developed two interoperability solutions to support workflow sharing: Coarse-Grained Interoperability (CGI) and Fine-Grained Interoperability (FGI). The project created the SHIWA Simulation Platform (SSP) to implement the Coarse-Grained Interoperability approach as a production-level service for research communities. The paper describes the CGI approach and how it enables sharing and combining existing workflows into complex applications and run them on Distributed Computing Infrastructures. The paper also outlines the architecture, components and usage scenarios of the simulation platform.

Analyzing and Modeling Medical Data on Distributed Computing Infrastructures

Researchers want to analyse Health Care data which may requires large pools of compute and data resources. To have them they need access to Distributed Computing Infrastructures (DCI). To use them it requires expertise which researchers may not have. Workflows can hide infrastructures. There are many workflow systems but they are not interoperable. To learn a workflow system and create workflows in a workflow system may require significant effort. Considering these efforts it is not reasonable to expect that researchers will learn new workflow systems if they want to run workflows of other workflow systems. As a result, the lack of interoperability prevents workflow sharing and a vast amount of research efforts is wasted. The FP7 Sharing Interoperable Workflow for Large-Scale Scientific Simulation on Available DCIs (SHIWA) project developed the Coarse-Grained Interoperability (CGI) to enable workflow sharing. The project created the SHIWA Simulation Platform (SSP) to support CGI as a production-level service. The paper describes how the CGI approach can be used for analysis and simulation in Health Care.

A Formal Approach to Support Interoperability in Scientific Meta-workflows

Scientific workflows orchestrate the execution of complex experiments frequently using distributed computing platforms. Meta-workflows represent an emerging type of such workflows which aim to reuse existing workflows from potentially different workflow systems to achieve more complex and experimentation minimizing workflow design and testing efforts. Workflow interoperability plays a profound role in achieving this objective. This paper is focused at fostering interoperability across meta-workflows that combine workflows of different workflow systems from diverse scientific domains. This is achieved by formalizing definitions of meta-workflow and its different types to standardize their data structures used to describe workflows to be published and shared via public repositories. The paper also includes thorough formalization of two workflow interoperability approaches based on this formal description: the coarse-grained and fine-grained workflow interoperability approach. The paper presents a case study from Astrophysics which successfully demonstrates the use of the concepts of meta-workflows and workflow interoperability within a scientific simulation platform.

Supporting dynamic parameter sweep applications in workflows: lessons learnt from the CancerGrid project

A vast majority of scientific grid applications are either parameter sweep applications or a significant subpart of these applications belong to class of parameter sweep activities. The paper describes a new graphical workflow language in which any node of the DAG-based workflow can be a parameter sweep node and the execution of these nodes are transparently executed either in service grids or in desktop grids depending on the computational complexity of the workflow node. The new concept is supported by the CancerGrid portal that has been established for a chemist community.

Commercial Use of WS-PGRADE/gUSE

Although originally an academic and research product, the WS-PGRADE/gUSE framework is increasingly applied by commercial institutions too. Within the SCI-BUS project, several commercial gateways have been developed by various companies. WS-PGRADE/gUSE is also intensively used within another European research project, CloudSME (Cloud-based Simulation Platform for Manufacturing and Engineering). This chapter provides an overview and de-scribes in detail some commercial WS-PGRADE/gUSE based gateway implemen-tations. Two representative case studies from the SCI-BUS project, the Build and Test portal and the eDOX Archiver Gateway are introduced. An overview of WS-PGRADE/gUSE based gateways for running simulation applications in the cloud within the CloudSME project is also provided.

WS-PGRADE/gUSE in European Projects

Besides core project partners, the SCI-BUS project also supported several external user communities in developing and setting up customized science gateways. The focus was on large communities typically represented by other European research projects. However, smaller local efforts with the potential of generalizing the solution to wider communities were also supported. This chapter gives an overview of support activities related to user communities external to the SCI-BUS project. A generic overview of such activities is provided followed by the detailed description of three gateways developed in collaboration with European projects: the agINFRA Science Gateway for Workflows for agricultural research, the VERCE Science Gateway for seismology, and the DRIHM Science Gateway for weather research and forecasting.

Developing Science Gateways at Various Levels of Granularity Using WS-PGRADE/gUSE

Science gateways can provide access to distributed computing resources and applications at very different levels of granularity. Some gateways do not even hide the details of the underlying infrastructure, while on the other end some provide completely customized high-level interfaces to end-users. In this chapter the different granularity levels at which science gateways can be developed with WS-PGRADE/gUSE are analysed. The differences between these various granu-larity levels are also illustrated via the example of a molecular docking gateway and its four different implementations.

Sharing science gateway artefacts through repositories

Researchers want to run scientific experiments focusing on their disciplines. They do not want to know how and where the experiments are executed. Science gateways hide details by coordinating the execution of experiments using different infrastructures and workflow systems. ER-flow/SHIWA and SCI-BUS project developed repositories to share artefacts such as applications, portlets, workflows, etc. inside and among research communities. Sharing artefacts in re-positories enable gateway developers to reuse them when building a new gateway and/or creating a new application.

Business Models for Cloud Computing: Experiences from Developing Modeling & Simulation as a Service Applications in Industry

The potential of cloud computing is gaining significant interest in Modeling & Simulation (M&S). The underlying concept of using computing power as a utility is very attractive to users that can access state-of-the-art hardware and software without capital investment. Moreover, the cloud computing characteristics of rapid elasticity and the ability to scale up or down according to workload make it very attractive to numerous applications including M&S. Research and development work typically focuses on the implementation of cloud-based systems supporting M&S as a Service (MSaaS). Such systems are typically composed of a supply chain of technology services. How is the payment collected from the end-user and distributed to the stakeholders in the supply chain? We discuss the business aspects of developing a cloud platform for various M&S applications. Business models from the perspectives of the stakeholders involved in providing and using MSaaS and cloud computing are investigated and presented.

Affective Computing to Enhance E-Learning in Segregated Societies

According to UN Women, to build stronger economies, it is essential to empower women to participate fully in economic life across all sectors. Increasing women and girls’ education enhances their chances to participate in the labor market. In certain cultures, like in Saudi Arabia, women contribution to the public economy growth is very limited. According to the World Bank, less than 20 percent of the female population participate in the labor force. This low participation rate has many reasons. One of them, is the educational level and educational quality for females. Although Saudi Arabia has about thirty three universities, opportunities are still limited for women because of the restrictions of access put upon them. A mixture of local norms, traditions, social beliefs, and principles preventing women from receiving full benefits from the educational system. Gender segregation is one of the challenges that limits the women access for education. It causes a problem due to the shortage of female faculty throughout the country. To overcome this problem, male faculty are allowed to teach female students under certain regulations and following a certain method of education delivery and interaction. However, most of these methods lack face-to-face communication between the teacher and students, which lowers the interactivity level and, accordingly, the students’ engagement, and increases the need for other alternatives. The e-learning model is one of high benefit for female students in such societies. Recognizing the students’ engagement is not straightforward in the e-learning model. To measure the level of engagement, the learner’s mood or emotions should be taken into consideration to help understanding and judging the level of engagement. This paper is to investigate the relationship between emotions and engagement in the e-learning environment, and how recognizing the learner’s emotions and change the content delivery accordingly can affect the efficiency of the e-learning process. The proposed experiment alluded to herein should help to find ways to increase the engagement of the learners, hence, enhance the efficiency of the learning process and the quality of learning, which will increase the chances and opportunities for women in such societies to participate more effectively in the labor market.

Distributed Agent-Based Load Balancer for Cloud Computing

In this paper we present a concept of an agent-based strategy to allocate services on a Cloud system without overloading nodes and maintaining the system stability with minimum cost. To provide a base for our research we specify an abstract model of cloud resources utilization, including multiple types of resources as well as considerations for the service migration costs. We also present an early version of simulation environment and a prototype of agent-based load balancer implemented in functional language Scala and Akka framework.

A Meta-Heuristic Load Balancer for Cloud Computing Systems

This paper introduces a strategy to allocate services on a cloud system without overloading the nodes and maintaining the system stability with minimum cost. We specify an abstract model of cloud resources utilization, including multiple types of resources as well as considerations for the service migration costs. A prototype meta-heuristic load balancer is demonstrated and experimental results are presented and discussed. We also propose a novel genetic algorithm, where population is seeded with the outputs of other meta-heuristic algorithms.