Challenges of cloud technology in manufacturing environment

The rapid growth Information systems and advanced network technologies have significant impact on enterprises around the world. Enterprises are trying to gain competitive advantage in open global markets by using the latest technologies, along with advanced networks, to create collaboration, reduce costs, and maximize productivity. The combination of latest technologies and advanced manufacturing networks technologies lead to growth of new manufacturing model named Cloud Manufacturing which can shift the manufacturing industry from product-oriented manufacturing to services-oriented manufacturing. This paper explores the literature about the current Manufacturing problems, understands the concept of Cloud Computing Technology, introduces Cloud Manufacturing and its role in the enterprise, and investigates the obstacles and challenges of adopting Cloud Manufacturing in enterprises

An Innovative Workspace for The Cherenkov Telescope Array

The Cherenkov Telescope Array (CTA) is an initiative to build the next generation, ground-based gamma-ray observatories. We present a prototype workspace developed at INAF that aims at providing innovative solutions for the CTA community. The workspace leverages open source technologies providing web access to a set of tools widely used by the CTA community. Two different user interaction models, connected to an authentication and authorization infrastructure, have been implemented in this workspace. The first one is a workflow management system accessed via a science gateway (based on the Liferay platform) and the second one is an interactive virtual desktop environment. The integrated workflow system allows to run applications used in astronomy and physics researches into distributed computing infrastructures (ranging from clusters to grids and clouds). The interactive desktop environment allows to use many software packages without any installation on local desktops exploiting their native graphical user interfaces. The science gateway and the interactive desktop environment are connected to the authentication and authorization infrastructure composed by a Shibboleth identity provider and a Grouper authorization solution. The Grouper released attributes are consumed by the science gateway to authorize the access to specific web resources and the role management mechanism in Liferay provides the attribute-role mapping

A Tale of Two Data-Intensive Paradigms: Applications, Abstractions, and Architectures

Scientific problems that depend on processing large amounts of data require overcoming challenges in multiple areas: managing large-scale data distribution, co-placement and scheduling of data with compute resources, and storing and transferring large volumes of data. We analyze the ecosystems of the two prominent paradigms for data-intensive applications, hereafter referred to as the high-performance computing and the Apache-Hadoop paradigm. We propose a basis, common terminology and functional factors upon which to analyze the two approaches of both paradigms. We discuss the concept of "Big Data Ogres" and their facets as means of understanding and characterizing the most common application workloads found across the two paradigms. We then discuss the salient features of the two paradigms, and compare and contrast the two approaches. Specifically, we examine common implementation/approaches of these paradigms, shed light upon the reasons for their current "architecture" and discuss some typical workloads that utilize them. In spite of the significant software distinctions, we believe there is architectural similarity. We discuss the potential integration of different implementations, across the different levels and components. Our comparison progresses from a fully qualitative examination of the two paradigms, to a semi-quantitative methodology. We use a simple and broadly used Ogre (K-means clustering), characterize its performance on a range of representative platforms, covering several implementations from both paradigms. Our experiments provide an insight into the relative strengths of the two paradigms. We propose that the set of Ogres will serve as a benchmark to evaluate the two paradigms along different dimensions.Comment: 8 pages, 2 figure

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure

Big data research has attracted great attention in science, technology, industry and society. It is developing with the evolving scientific paradigm, the fourth industrial revolution, and the transformational innovation of technologies. However, its nature and fundamental challenge have not been recognized, and its own methodology has not been formed. This paper explores and answers the following questions: What is big data? What are the basic methods for representing, managing and analyzing big data? What is the relationship between big data and knowledge? Can we find a mapping from big data into knowledge space? What kind of infrastructure is required to support not only big data management and analysis but also knowledge discovery, sharing and management? What is the relationship between big data and science paradigm? What is the nature and fundamental challenge of big data computing? A multi-dimensional perspective is presented toward a methodology of big data computing.Comment: 59 page