JXTA-Overlay: a P2P platform for distributed, collaborative, and ubiquitous computing

With the fast growth of the Internet infrastructure and the use of large-scale complex applications in industries, transport, logistics, government, health, and businesses, there is an increasing need to design and deploy multifeatured networking applications. Important features of such applications include the capability to be self-organized, be decentralized, integrate different types of resources (personal computers, laptops, and mobile and sensor devices), and provide global, transparent, and secure access to resources. Moreover, such applications should support not only traditional forms of reliable distributing computing and optimization of resources but also various forms of collaborative activities, such as business, online learning, and social networks in an intelligent and secure environment. In this paper, we present the Juxtapose (JXTA)-Overlay, which is a JXTA-based peer-to-peer (P2P) platform designed with the aim to leverage capabilities of Java, JXTA, and P2P technologies to support distributed and collaborative systems. The platform can be used not only for efficient and reliable distributed computing but also for collaborative activities and ubiquitous computing by integrating in the platform end devices. The design of a user interface as well as security issues are also tackled. We evaluate the proposed system by experimental study and show its usefulness for massive processing computations and e-learning applications.Peer ReviewedPostprint (author's final draft

Enhancing Job Scheduling of an Atmospheric Intensive Data Application

Nowadays, e-Science applications involve great deal of data to have more accurate analysis. One of its application domains is the Radio Occultation which manages satellite data. Grid Processing Management is a physical infrastructure geographically distributed based on Grid Computing, that is implemented for the overall processing Radio Occultation analysis. After a brief description of algorithms adopted to characterize atmospheric profiles, the paper presents an improvement of job scheduling in order to decrease processing time and optimize resource utilization. Extension of grid computing capacity is implemented by virtual machines in existing physical Grid in order to satisfy temporary job requests. Also scheduling plays an important role in the infrastructure that is handled by a couple of schedulers which are developed to manage data automaticall

The Knowledge of the Grid: A Grid Ontology

This paper presents a knowledge architecture and set of ontologies that can be used as the foundation to facilitate the matching of abstract resource requests to services and resources, to determine the functional equivalence of Grid middle wares and deployments and to allow the description of ‘hybrid’ compound Grids composed of individual heterogeneous Grids. This is necessary as in all these cases what is required is mediation between different views or descriptions of Grids, which requires a formal reference vocabulary. We present a framework and ontologies for achieving this

Adaptive online deployment for resource constrained mobile smart clients

Nowadays mobile devices are more and more used as a platform for applications. Contrary to prior generation handheld devices configured with a predefined set of applications, today leading edge devices provide a platform for flexible and customized application deployment. However, these applications have to deal with the limitations (e.g. CPU speed, memory) of these mobile devices and thus cannot handle complex tasks. In order to cope with the handheld limitations and the ever changing device context (e.g. network connections, remaining battery time, etc.) we present a middleware solution that dynamically offloads parts of the software to the most appropriate server. Without a priori knowledge of the application, the optimal deployment is calculated, that lowers the cpu usage at the mobile client, whilst keeping the used bandwidth minimal. The information needed to calculate this optimum is gathered on the fly from runtime information. Experimental results show that the proposed solution enables effective execution of complex applications in a constrained environment. Moreover, we demonstrate that the overhead from the middleware components is below 2%

Cloud-Based Security Driven Human Resource Management System

With the emergence of cloud computing, it has become easy to store large volumes of data in the cloud to enhance Human Resource Management (HRM), based on the elasticity and scalability that cloud computing offers. This paper proposes the OnibereOdunayoSecurity-4 (OOS-4) security model for Human Resource Information System (HRIS) deployed on a cloud platform. The OOS-4 framework is a holistic and integrated model that is expected to allow for better interrelatedness of the various components of a HRM organization with adequate consideration for security. Furthermore, utilizing the Platform as a Service (PaaS) cloud computing architecture, the model was implemented using the Google App Engine. The result is a scalable application in which the data in storage is encrypted and visible on the Google Cloud Platform data store. The application is secured by proving encryption for data in storage on the Google Cloud Platform. The application will enhance HRM

AstroGrid-D: Enhancing Astronomic Science with Grid Technology

We present AstroGrid-D, a project bringing together astronomers and experts in Grid technology to enhance astronomic science in many aspects. First, by sharing currently dispersed resources, scientists can calculate their models in more detail. Second, by developing new mechanisms to efficiently access and process existing datasets, scientific problems can be investigated that were until now impossible to solve. Third, by adopting Grid technology large instruments such as robotic telescopes and complex scientific workflows from data aquisition to analysis can be managed in an integrated manner. In this paper, we present prominent astronomic use cases, discuss requirements on a Grid middleware and present our approach to extend/augment existing middleware to facilitate the improvements mentioned above

SciTokens: Capability-Based Secure Access to Remote Scientific Data

The management of security credentials (e.g., passwords, secret keys) for computational science workflows is a burden for scientists and information security officers. Problems with credentials (e.g., expiration, privilege mismatch) cause workflows to fail to fetch needed input data or store valuable scientific results, distracting scientists from their research by requiring them to diagnose the problems, re-run their computations, and wait longer for their results. In this paper, we introduce SciTokens, open source software to help scientists manage their security credentials more reliably and securely. We describe the SciTokens system architecture, design, and implementation addressing use cases from the Laser Interferometer Gravitational-Wave Observatory (LIGO) Scientific Collaboration and the Large Synoptic Survey Telescope (LSST) projects. We also present our integration with widely-used software that supports distributed scientific computing, including HTCondor, CVMFS, and XrootD. SciTokens uses IETF-standard OAuth tokens for capability-based secure access to remote scientific data. The access tokens convey the specific authorizations needed by the workflows, rather than general-purpose authentication impersonation credentials, to address the risks of scientific workflows running on distributed infrastructure including NSF resources (e.g., LIGO Data Grid, Open Science Grid, XSEDE) and public clouds (e.g., Amazon Web Services, Google Cloud, Microsoft Azure). By improving the interoperability and security of scientific workflows, SciTokens 1) enables use of distributed computing for scientific domains that require greater data protection and 2) enables use of more widely distributed computing resources by reducing the risk of credential abuse on remote systems.Comment: 8 pages, 6 figures, PEARC '18: Practice and Experience in Advanced Research Computing, July 22--26, 2018, Pittsburgh, PA, US