58,215 research outputs found
A Cloud-Oriented Green Computing Architecture for E-Learning Applications
Cloud computing is a highly scalable and cost-effective infrastructure for running Web applications. E-learning or e-Learning is one of such Web application has increasingly gained popularity in the recent years, as a comprehensive medium of global education system/training systems. The development of e-Learning Application within the cloud computing environment enables users to access diverse software applications, share data, collaborate more easily, and keep their data safely in the infrastructure. However, the growing demand of Cloud infrastructure has drastically increased the energy consumption of data centers, which has become a critical issue. High energy consumption not only translates to high operational cost, which reduces the profit margin of Cloud providers, but also leads to high carbon emissions which is not environmentally friendly. Hence, energy-efficient solutions are required to minimize the impact of Cloud-Oriented E-Learning on the environment. E-learning methods have drastically changed the educational environment and also reduced the use of papers and ultimately reduce the production of carbon footprint. E-learning methodology is an example of Green computing. Thus, in this paper, it is proposed a Cloud-Oriented Green Computing Architecture for eLearning Applications (COGALA). The e-Learning Applications using COGALA can lower expenses, reduce energy consumption, and help organizations with limited IT resources to deploy and maintain needed software in a timely manner. This paper also discussed the implication of this solution for future research directions to enable Cloud-Oriented Green Computing
Cloud computing resource scheduling and a survey of its evolutionary approaches
A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon
Challenges for the comprehensive management of cloud services in a PaaS framework
The 4CaaSt project aims at developing a PaaS framework that enables flexible definition, marketing, deployment and management of Cloud-based services and applications. The major innovations proposed by 4CaaSt are the blueprint and its lifecycle management, a one stop shop for Cloud services and a PaaS level resource management featuring elasticity. 4CaaSt also provides a portfolio of ready to use Cloud native services and Cloud-aware immigrant technologies
Storage Solutions for Big Data Systems: A Qualitative Study and Comparison
Big data systems development is full of challenges in view of the variety of
application areas and domains that this technology promises to serve.
Typically, fundamental design decisions involved in big data systems design
include choosing appropriate storage and computing infrastructures. In this age
of heterogeneous systems that integrate different technologies for optimized
solution to a specific real world problem, big data system are not an exception
to any such rule. As far as the storage aspect of any big data system is
concerned, the primary facet in this regard is a storage infrastructure and
NoSQL seems to be the right technology that fulfills its requirements. However,
every big data application has variable data characteristics and thus, the
corresponding data fits into a different data model. This paper presents
feature and use case analysis and comparison of the four main data models
namely document oriented, key value, graph and wide column. Moreover, a feature
analysis of 80 NoSQL solutions has been provided, elaborating on the criteria
and points that a developer must consider while making a possible choice.
Typically, big data storage needs to communicate with the execution engine and
other processing and visualization technologies to create a comprehensive
solution. This brings forth second facet of big data storage, big data file
formats, into picture. The second half of the research paper compares the
advantages, shortcomings and possible use cases of available big data file
formats for Hadoop, which is the foundation for most big data computing
technologies. Decentralized storage and blockchain are seen as the next
generation of big data storage and its challenges and future prospects have
also been discussed
Research and Development Workstation Environment: the new class of Current Research Information Systems
Against the backdrop of the development of modern technologies in the field
of scientific research the new class of Current Research Information Systems
(CRIS) and related intelligent information technologies has arisen. It was
called - Research and Development Workstation Environment (RDWE) - the
comprehensive problem-oriented information systems for scientific research and
development lifecycle support. The given paper describes design and development
fundamentals of the RDWE class systems. The RDWE class system's generalized
information model is represented in the article as a three-tuple composite web
service that include: a set of atomic web services, each of them can be
designed and developed as a microservice or a desktop application, that allows
them to be used as an independent software separately; a set of functions, the
functional filling-up of the Research and Development Workstation Environment;
a subset of atomic web services that are required to implement function of
composite web service. In accordance with the fundamental information model of
the RDWE class the system for supporting research in the field of ontology
engineering - the automated building of applied ontology in an arbitrary domain
area, scientific and technical creativity - the automated preparation of
application documents for patenting inventions in Ukraine was developed. It was
called - Personal Research Information System. A distinctive feature of such
systems is the possibility of their problematic orientation to various types of
scientific activities by combining on a variety of functional services and
adding new ones within the cloud integrated environment. The main results of
our work are focused on enhancing the effectiveness of the scientist's research
and development lifecycle in the arbitrary domain area.Comment: In English, 13 pages, 1 figure, 1 table, added references in Russian.
Published. Prepared for special issue (UkrPROG 2018 conference) of the
scientific journal "Problems of programming" (Founder: National Academy of
Sciences of Ukraine, Institute of Software Systems of NAS Ukraine
- …