3,830 research outputs found
A Uniform Approach to Programming the World Wide Web
We propose a uniform model for programming distributed Web applications. The model is based on the concept of Web computation places and provides mechanisms to coordinate distributed computations at these places, including peer-to-peer communication between places and a uniform mechanism to initiate computation in remote places. Computations can interact with the flow of HTTP requests and responses, typically as clients, proxies or servers in the Web architecture. We have implemented the model using the global pointers and remote service requests provided by the Nexus communication library. We present the model and its rationale, with some illustrative examples, and we describe the implementation
MOSDEN: A Scalable Mobile Collaborative Platform for Opportunistic Sensing Applications
Mobile smartphones along with embedded sensors have become an efficient
enabler for various mobile applications including opportunistic sensing. The
hi-tech advances in smartphones are opening up a world of possibilities. This
paper proposes a mobile collaborative platform called MOSDEN that enables and
supports opportunistic sensing at run time. MOSDEN captures and shares sensor
data across multiple apps, smartphones and users. MOSDEN supports the emerging
trend of separating sensors from application-specific processing, storing and
sharing. MOSDEN promotes reuse and re-purposing of sensor data hence reducing
the efforts in developing novel opportunistic sensing applications. MOSDEN has
been implemented on Android-based smartphones and tablets. Experimental
evaluations validate the scalability and energy efficiency of MOSDEN and its
suitability towards real world applications. The results of evaluation and
lessons learned are presented and discussed in this paper.Comment: Accepted to be published in Transactions on Collaborative Computing,
2014. arXiv admin note: substantial text overlap with arXiv:1310.405
Uintah: a massively parallel problem solving environment
Journal ArticleThis paper describes Uintah, a component-based visual problem solving environment (PSE) that is designed to specifically address the unique problems of massively parallel computation on terascale computing platforms. Uintah supports the entire life cycle of scientific applications by allowing scientific programmers to quickly and easily develop new techniques, debug new implementations, and apply known algorithms to solve novel problems. Uintah is built on three principles: 1) As much as possible, the complexities of parallel execution should be handled for the scientist, 2) software should be reusable at the component level, and 3) scientists should be able to dynamically steer and visualize their simulation results as the simulation executes. To provide this functionality, Uintah builds upon the best features of the SCIRun PSE and the DOE Common Component Architecture (CCA)
Economic Growth and Electricity Consumption in a Multivariate Framework: A Case of Zimbabwe 1980 to 2016
Electricity is important for sustainable development as it enhances productivity, employment and general living standards of people. The paper investigates the relationship between electricity consumption and economic growth in a multivariate framework for the period 1980 to 2016 in Zimbabwe. The study builds on previous bi-variate studies on electricity consumption and economic growth nexus. Specifically, the study applies both granger causality tests and single step error correction model to study the relationship between electricity consumption, economic growth and investment. The Granger causality tests confirm the existence of a bi-directional causality between electricity consumption and economic growth. This implies that in Zimbabwe, electricity growth results in increased economic growth and vice versa. Electricity also granger causes investment in Zimbabwe. The study shows that there is a long run relationship between electricity, investment and economic growth in Zimbabwe. The results suggest that consumption of electricity is a prerequisite and a binding constraint to achieving higher economic growth in Zimbabwe. In order to boost economic growth, the country needs to profoundly invest in electricity infrastructure. 
Global Grids and Software Toolkits: A Study of Four Grid Middleware Technologies
Grid is an infrastructure that involves the integrated and collaborative use
of computers, networks, databases and scientific instruments owned and managed
by multiple organizations. Grid applications often involve large amounts of
data and/or computing resources that require secure resource sharing across
organizational boundaries. This makes Grid application management and
deployment a complex undertaking. Grid middlewares provide users with seamless
computing ability and uniform access to resources in the heterogeneous Grid
environment. Several software toolkits and systems have been developed, most of
which are results of academic research projects, all over the world. This
chapter will focus on four of these middlewares--UNICORE, Globus, Legion and
Gridbus. It also presents our implementation of a resource broker for UNICORE
as this functionality was not supported in it. A comparison of these systems on
the basis of the architecture, implementation model and several other features
is included.Comment: 19 pages, 10 figure
Efficient Opportunistic Sensing using Mobile Collaborative Platform MOSDEN
Mobile devices are rapidly becoming the primary computing device in people's
lives. Application delivery platforms like Google Play, Apple App Store have
transformed mobile phones into intelligent computing devices by the means of
applications that can be downloaded and installed instantly. Many of these
applications take advantage of the plethora of sensors installed on the mobile
device to deliver enhanced user experience. The sensors on the smartphone
provide the opportunity to develop innovative mobile opportunistic sensing
applications in many sectors including healthcare, environmental monitoring and
transportation. In this paper, we present a collaborative mobile sensing
framework namely Mobile Sensor Data EngiNe (MOSDEN) that can operate on
smartphones capturing and sharing sensed data between multiple distributed
applications and users. MOSDEN follows a component-based design philosophy
promoting reuse for easy and quick opportunistic sensing application
deployments. MOSDEN separates the application-specific processing from the
sensing, storing and sharing. MOSDEN is scalable and requires minimal
development effort from the application developer. We have implemented our
framework on Android-based mobile platforms and evaluate its performance to
validate the feasibility and efficiency of MOSDEN to operate collaboratively in
mobile opportunistic sensing applications. Experimental outcomes and lessons
learnt conclude the paper
Parallel programming paradigms and frameworks in big data era
With Cloud Computing emerging as a promising new approach for ad-hoc parallel data processing, major companies have started to integrate frameworks for parallel data processing in their product portfolio, making it easy for customers to access these services and to deploy their programs. We have entered the Era of Big Data. The explosion and profusion of available data in a wide range of application domains rise up new challenges and opportunities in a plethora of disciplines-ranging from science and engineering to biology and business. One major challenge is how to take advantage of the unprecedented scale of data-typically of heterogeneous nature-in order to acquire further insights and knowledge for improving the quality of the offered services. To exploit this new resource, we need to scale up and scale out both our infrastructures and standard techniques. Our society is already data-rich, but the question remains whether or not we have the conceptual tools to handle it. In this paper we discuss and analyze opportunities and challenges for efficient parallel data processing. Big Data is the next frontier for innovation, competition, and productivity, and many solutions continue to appear, partly supported by the considerable enthusiasm around the MapReduce paradigm for large-scale data analysis. We review various parallel and distributed programming paradigms, analyzing how they fit into the Big Data era, and present modern emerging paradigms and frameworks. To better support practitioners interesting in this domain, we end with an analysis of on-going research challenges towards the truly fourth generation data-intensive science.Peer ReviewedPostprint (author's final draft
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