11,337 research outputs found
Investigation into Mobile Learning Framework in Cloud Computing Platform
AbstractâCloud computing infrastructure is increasingly
used for distributed applications. Mobile learning
applications deployed in the cloud are a new research
direction. The applications require specific development
approaches for effective and reliable communication. This
paper proposes an interdisciplinary approach for design and
development of mobile applications in the cloud. The
approach includes front service toolkit and backend service
toolkit. The front service toolkit packages data and sends it
to a backend deployed in a cloud computing platform. The
backend service toolkit manages rules and workflow, and
then transmits required results to the front service toolkit.
To further show feasibility of the approach, the paper
introduces a case study and shows its performance
Technical Report on Deploying a highly secured OpenStack Cloud Infrastructure using BradStack as a Case Study
Cloud computing has emerged as a popular paradigm and an attractive model for
providing a reliable distributed computing model.it is increasing attracting
huge attention both in academic research and industrial initiatives. Cloud
deployments are paramount for institution and organizations of all scales. The
availability of a flexible, free open source cloud platform designed with no
propriety software and the ability of its integration with legacy systems and
third-party applications are fundamental. Open stack is a free and opensource
software released under the terms of Apache license with a fragmented and
distributed architecture making it highly flexible. This project was initiated
and aimed at designing a secured cloud infrastructure called BradStack, which
is built on OpenStack in the Computing Laboratory at the University of
Bradford. In this report, we present and discuss the steps required in
deploying a secured BradStack Multi-node cloud infrastructure and conducting
Penetration testing on OpenStack Services to validate the effectiveness of the
security controls on the BradStack platform. This report serves as a practical
guideline, focusing on security and practical infrastructure related issues. It
also serves as a reference for institutions looking at the possibilities of
implementing a secured cloud solution.Comment: 38 pages, 19 figures
RELEASE: A High-level Paradigm for Reliable Large-scale Server Software
Erlang is a functional language with a much-emulated model for building reliable distributed systems. This paper outlines the RELEASE project, and describes the progress in the first six months. The project aim is to scale the Erlangâs radical concurrency-oriented programming paradigm to build reliable general-purpose software, such as server-based systems, on massively parallel machines. Currently Erlang has inherently scalable computation and reliability models, but in practice scalability is constrained by aspects of the language and virtual machine. We are working at three levels to address these challenges: evolving the Erlang virtual machine so that it can work effectively on large scale multicore systems; evolving the language to Scalable Distributed (SD) Erlang; developing a scalable Erlang infrastructure to integrate multiple, heterogeneous clusters. We are also developing state of the art tools that allow programmers to understand the behaviour of massively parallel SD Erlang programs. We will demonstrate the effectiveness of the RELEASE approach using demonstrators and two large case studies on a Blue Gene
The Family of MapReduce and Large Scale Data Processing Systems
In the last two decades, the continuous increase of computational power has
produced an overwhelming flow of data which has called for a paradigm shift in
the computing architecture and large scale data processing mechanisms.
MapReduce is a simple and powerful programming model that enables easy
development of scalable parallel applications to process vast amounts of data
on large clusters of commodity machines. It isolates the application from the
details of running a distributed program such as issues on data distribution,
scheduling and fault tolerance. However, the original implementation of the
MapReduce framework had some limitations that have been tackled by many
research efforts in several followup works after its introduction. This article
provides a comprehensive survey for a family of approaches and mechanisms of
large scale data processing mechanisms that have been implemented based on the
original idea of the MapReduce framework and are currently gaining a lot of
momentum in both research and industrial communities. We also cover a set of
introduced systems that have been implemented to provide declarative
programming interfaces on top of the MapReduce framework. In addition, we
review several large scale data processing systems that resemble some of the
ideas of the MapReduce framework for different purposes and application
scenarios. Finally, we discuss some of the future research directions for
implementing the next generation of MapReduce-like solutions.Comment: arXiv admin note: text overlap with arXiv:1105.4252 by other author
- âŚ