8,256 research outputs found
Survey on Additive Manufacturing, Cloud 3D Printing and Services
Cloud Manufacturing (CM) is the concept of using manufacturing resources in a
service oriented way over the Internet. Recent developments in Additive
Manufacturing (AM) are making it possible to utilise resources ad-hoc as
replacement for traditional manufacturing resources in case of spontaneous
problems in the established manufacturing processes. In order to be of use in
these scenarios the AM resources must adhere to a strict principle of
transparency and service composition in adherence to the Cloud Computing (CC)
paradigm. With this review we provide an overview over CM, AM and relevant
domains as well as present the historical development of scientific research in
these fields, starting from 2002. Part of this work is also a meta-review on
the domain to further detail its development and structure
Modern software cybernetics: new trends
Software cybernetics research is to apply a variety of techniques from cybernetics research to software engineering research. For more than fifteen years since 2001, there has been a dramatic increase in work relating to software cybernetics. From cybernetics viewpoint, the work is mainly on the first-order level, namely, the software under observation and control. Beyond the first-order cybernetics, the software, developers/users, and running environments influence each other and thus create feedback to form more complicated systems. We classify software cybernetics as Software Cybernetics I based on the first-order cybernetics, and as Software Cybernetics II based on the higher order cybernetics. This paper provides a review of the literature on software cybernetics, particularly focusing on the transition from Software Cybernetics I to Software Cybernetics II. The results of the survey indicate that some new research areas such as Internet of Things, big data, cloud computing, cyber-physical systems, and even creative computing are related to Software Cybernetics II. The paper identifies the relationships between the techniques of Software Cybernetics II applied and the new research areas to which they have been applied, formulates research problems and challenges of software cybernetics with the application of principles of Phase II of software cybernetics; identifies and highlights new research trends of software cybernetic for further research
Modern software cybernetics: New trends
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Software cybernetics research is to apply a variety of techniques from cybernetics research to software engineering research. For more than fifteen years since 2001, there has been a dramatic increase in work relating to software cybernetics. From cybernetics viewpoint, the work is mainly on the first-order level, namely, the software under observation and control. Beyond the first-order cybernetics, the software, developers/users, and running environments influence each other and thus create feedback to form more complicated systems. We classify software cybernetics as Software Cybernetics I based on the first-order cybernetics, and as Software Cybernetics II based on the higher order cybernetics. This paper provides a review of the literature on software cybernetics, particularly focusing on the transition from Software Cybernetics I to Software Cybernetics II. The results of the survey indicate that some new research areas such as Internet of Things, big data, cloud computing, cyber-physical systems, and even creative computing are related to Software Cybernetics II. The paper identifies the relationships between the techniques of Software Cybernetics II applied and the new research areas to which they have been applied, formulates research problems and challenges of software cybernetics with the application of principles of Phase II of software cybernetics; identifies and highlights new research trends of software cybernetic for further research
Ecosystem-Oriented Distributed Evolutionary Computing
We create a novel optimisation technique inspired by natural ecosystems,
where the optimisation works at two levels: a first optimisation, migration of
genes which are distributed in a peer-to-peer network, operating continuously
in time; this process feeds a second optimisation based on evolutionary
computing that operates locally on single peers and is aimed at finding
solutions to satisfy locally relevant constraints. We consider from the domain
of computer science distributed evolutionary computing, with the relevant
theory from the domain of theoretical biology, including the fields of
evolutionary and ecological theory, the topological structure of ecosystems,
and evolutionary processes within distributed environments. We then define
ecosystem- oriented distributed evolutionary computing, imbibed with the
properties of self-organisation, scalability and sustainability from natural
ecosystems, including a novel form of distributed evolu- tionary computing.
Finally, we conclude with a discussion of the apparent compromises resulting
from the hybrid model created, such as the network topology.Comment: 8 pages, 5 figures. arXiv admin note: text overlap with
arXiv:1112.0204, arXiv:0712.4159, arXiv:0712.4153, arXiv:0712.4102,
arXiv:0910.067
An Integrated Framework for the Methodological Assurance of Security and Privacy in the Development and Operation of MultiCloud Applications
x, 169 p.This Thesis studies research questions about how to design multiCloud applications taking into account security and privacy requirements to protect the system from potential risks and about how to decide which security and privacy protections to include in the system. In addition, solutions are needed to overcome the difficulties in assuring security and privacy properties defined at design time still hold all along the system life-cycle, from development to operation.In this Thesis an innovative DevOps integrated methodology and framework are presented, which help to rationalise and systematise security and privacy analyses in multiCloud to enable an informed decision-process for risk-cost balanced selection of the protections of the system components and the protections to request from Cloud Service Providers used. The focus of the work is on the Development phase of the analysis and creation of multiCloud applications.The main contributions of this Thesis for multiCloud applications are four: i) The integrated DevOps methodology for security and privacy assurance; and its integrating parts: ii) a security and privacy requirements modelling language, iii) a continuous risk assessment methodology and its complementary risk-based optimisation of defences, and iv) a Security and Privacy Service Level AgreementComposition method.The integrated DevOps methodology and its integrating Development methods have been validated in the case study of a real multiCloud application in the eHealth domain. The validation confirmed the feasibility and benefits of the solution with regards to the rationalisation and systematisation of security and privacy assurance in multiCloud systems
A Time-driven Data Placement Strategy for a Scientific Workflow Combining Edge Computing and Cloud Computing
Compared to traditional distributed computing environments such as grids,
cloud computing provides a more cost-effective way to deploy scientific
workflows. Each task of a scientific workflow requires several large datasets
that are located in different datacenters from the cloud computing environment,
resulting in serious data transmission delays. Edge computing reduces the data
transmission delays and supports the fixed storing manner for scientific
workflow private datasets, but there is a bottleneck in its storage capacity.
It is a challenge to combine the advantages of both edge computing and cloud
computing to rationalize the data placement of scientific workflow, and
optimize the data transmission time across different datacenters. Traditional
data placement strategies maintain load balancing with a given number of
datacenters, which results in a large data transmission time. In this study, a
self-adaptive discrete particle swarm optimization algorithm with genetic
algorithm operators (GA-DPSO) was proposed to optimize the data transmission
time when placing data for a scientific workflow. This approach considered the
characteristics of data placement combining edge computing and cloud computing.
In addition, it considered the impact factors impacting transmission delay,
such as the band-width between datacenters, the number of edge datacenters, and
the storage capacity of edge datacenters. The crossover operator and mutation
operator of the genetic algorithm were adopted to avoid the premature
convergence of the traditional particle swarm optimization algorithm, which
enhanced the diversity of population evolution and effectively reduced the data
transmission time. The experimental results show that the data placement
strategy based on GA-DPSO can effectively reduce the data transmission time
during workflow execution combining edge computing and cloud computing
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