Experiences with writing grid clients for mobile devices

Effective e-learning distributed environments should promote high cooperation. Workflow techniques can certainly contribute to such effectiveness. Reusable Learning Objects (RLOs or LOs) also play an important role in this context as learning content is broken into smaller parts to facilitate deployment and execution assignment. Additionally, some LOs may require high levels of computation as in the case of simulations in Hemodynamics in a Fluid Mechanics course. In order to cope with such an application, we envisioned a workflow-based Learning Management System (LMS) using web services as the communication infrastructure allied to the computational power of the Grid.

2005

Grid technologies promise to improve the way we think about e-learning allowing wide-scale learning resources sharing in heterogeneous and geographically distributed environments consenting, in this way, the implementation of distributed learning spaces where different organizations and individuals are able to cooperate in pursuing similar and complementary learning and training objectives. But is the e-learning ready for this evolution? In this paper we try, starting from an existing e-learning platform named IWT, to sketch a possible migration path toward a Grid based environment. IWT was selected because it presents a flexible, service-oriented, layered architecture suitable for migration in an OGSA compliant environment. The new approach will provide more flexibility, in fact, it will possible to leverage on the resources distributed across the Grid in order to build the learning experience that best fit student requirements. A use case scenario is also provided in order to emphasize differences between the two approaches. After a brief introduction of Grid and Learning technologies (section 1), a brief summary of IWT is given and the execution scenario is shown (section 2). A possible OGSA-based deployment of IWT is then sketched together with the new version of the execution scenario (section 3). Some conclusions are, then, presented (section 4).

Lecture Notes in Computer Science, 2005

In this paper we present an architecture of a middleware layer 1 that enables users of mobile devices to seamlessly and securely access distributed resources in a Grid. It lays the ground work for an application toolkit that addresses issues such as delegation of the job to the Grid service, interaction with heterogeneous mobile devices, support for offline processing, secure communication between the client and the middleware and presentation of results formatted in accordance with the device specification by outsourcing computationally intensive tasks with high storage and network bandwidth demands.

2005

Abstract One of the visions of grid computing is to access computational resources automatically on demand to deliver the services required with appropriate quality. Because mobile devices are now increasingly common, an infrastructure is required to allow mobile devices to use grid services, and thus enable the execution of complex resource-intensive applications on the resource-constrained devices. We present a system infrastructure that allows local mobile devices to interact with the grid.

J. Ubiquitous Syst. Pervasive Networks, 2015

The overall aim of this paper is to introduce Personal Mobile Grids (PM-Grids) as a novel paradigm in grid computing that eases scaling grid infrastructures to mobile devices and extending grid users to individuals outside research and enterprise domains. In this paper, architectural designs and simulation models for PM-Grids are presented as well as a honeybee inspired resource scheduling heuristic incorporating a radical approach to grid schedulers. A detailed design and implementation of HoPe with a decentralised self-management and adaptive policy are presented. PM-Grid designs and HoPe implementation were evaluated thoroughly through a strictly controlled empirical evaluation framework with a well-established heuristic in high throughput computing, the opportunistic scheduling heuristic (OSH), as a benchmark algorithm. Experimental results demonstrated the superiority of HoPe performance in terms of stability, throughput and turnaround time, under different running conditions of number of jobs and grid scales.

2005

Grid technologies promise to improve the way we think about e-learning allowing wide-scale learning resources sharing in heterogeneous and geographically distributed environments consenting, in this way, the implementation of distributed learning spaces where different organizations and individuals are able to cooperate in pursuing similar and complementary learning and training objectives. But is the e-learning ready for this evolution? In this paper we try, starting from an existing e-learning platform named IWT, to sketch a possible migration path toward a Grid based environment. IWT was selected because it presents a flexible, service-oriented, layered architecture suitable for migration in an OGSA compliant environment. The new approach will provide more flexibility, in fact, it will possible to leverage on the resources distributed across the Grid in order to build the learning experience that best fit student requirements. A use case scenario is also provided in order to emphasize differences between the two approaches. After a brief introduction of Grid and Learning technologies (section 1), a brief summary of IWT is given and the execution scenario is shown (section 2). A possible OGSA-based deployment of IWT is then sketched together with the new version of the execution scenario (section 3). Some conclusions are, then, presented (section 4).

Lecture Notes in Computer Science, 2005

Currently, access to Grid services is limited to resourceful devices such as desktop PCs but most mobile devices (with wireless network connections) cannot access the Grid network directly because of their resource limitations. Yet, extending the potential of the Grid to a wider audience promises increase in flexible usage and productivity. In this paper we present a middleware architecture 1 that addresses the issues of job delegation to a Grid service, support for offline processing, secure communication, interaction with heterogeneous mobile devices and presentation of results formatted in accordance with the device specification. This is achieved by outsourcing the resource intensive tasks from the mobile device to the middleware. We also demonstrate through formal modeling using Petri nets that the addition of such a middleware causes minimum overhead and the benefits obtained outweigh this overhead.

Personal and Ubiquitous Computing, 2009

This paper is centred on one of the main results of the ELeGI project, namely its software architecture for the delivery of personalised formal-learning experiences. The architecture has been designed and developed: (1) taking into account a general model for the personalisation of learning experiences, allowing us to obtain a solution that is flexible with respect to the pedagogies, and (2) on top of service oriented grid technologies, allowing us to obtain several advantages in the process of creation and delivery of personalised learning experience like, for instance, ubiquitous and seamless access to heterogeneous learning resources distributed over the network. In order to validate our result, the first prototype of the ELeGI architecture has been deployed on a virtual organisation consisting of three geographically distributed nodes. Each node of the VO provides services and learning resources that have been adopted in the creation and delivery of a personalised learning experience about the Torricelli’s law and based on the virtual scientific experiment model. The case of study has been successfully executed and has given us a proof of our assumptions related to the added value of the service oriented grid mainly in terms of: (1) capabilities to access educational resources distributed over the network, that is relevant in achieving the personalisation of learning experiences, and (2) high level of dynamicity and adaptiveness in the creation and delivery processes of a personalised learning experience.