14,504 research outputs found
The Ultralight project: the network as an integrated and managed resource for data-intensive science
Looks at the UltraLight project which treats the network interconnecting globally distributed data sets as a dynamic, configurable, and closely monitored resource to construct a next-generation system that can meet the high-energy physics community's data-processing, distribution, access, and analysis needs
Real-Time Data Processing With Lambda Architecture
Data has evolved immensely in recent years, in type, volume and velocity. There are several frameworks to handle the big data applications. The project focuses on the Lambda Architecture proposed by Marz and its application to obtain real-time data processing. The architecture is a solution that unites the benefits of the batch and stream processing techniques. Data can be historically processed with high precision and involved algorithms without loss of short-term information, alerts and insights. Lambda Architecture has an ability to serve a wide range of use cases and workloads that withstands hardware and human mistakes. The layered architecture enhances loose coupling and flexibility in the system. This a huge benefit that allows understanding the trade-offs and application of various tools and technologies across the layers. There has been an advancement in the approach of building the LA due to improvements in the underlying tools. The project demonstrates a simplified architecture for the LA that is maintainable
Enabling stream processing for people-centric IoT based on the fog computing paradigm
The world of machine-to-machine (M2M) communication is gradually moving from vertical single purpose solutions to multi-purpose and collaborative applications interacting across industry verticals, organizations and people - A world of Internet of Things (IoT). The dominant approach for delivering IoT applications relies on the development of cloud-based IoT platforms that collect all the data generated by the sensing elements and centrally process the information to create real business value. In this paper, we present a system that follows the Fog Computing paradigm where the sensor resources, as well as the intermediate layers between embedded devices and cloud computing datacenters, participate by providing computational, storage, and control. We discuss the design aspects of our system and present a pilot deployment for the evaluating the performance in a real-world environment. Our findings indicate that Fog Computing can address the ever-increasing amount of data that is inherent in an IoT world by effective communication among all elements of the architecture
Issues about the Adoption of Formal Methods for Dependable Composition of Web Services
Web Services provide interoperable mechanisms for describing, locating and
invoking services over the Internet; composition further enables to build
complex services out of simpler ones for complex B2B applications. While
current studies on these topics are mostly focused - from the technical
viewpoint - on standards and protocols, this paper investigates the adoption of
formal methods, especially for composition. We logically classify and analyze
three different (but interconnected) kinds of important issues towards this
goal, namely foundations, verification and extensions. The aim of this work is
to individuate the proper questions on the adoption of formal methods for
dependable composition of Web Services, not necessarily to find the optimal
answers. Nevertheless, we still try to propose some tentative answers based on
our proposal for a composition calculus, which we hope can animate a proper
discussion
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