3,092 research outputs found
Cloud service discovery and analysis: a unified framework
Over the past few years, cloud computing has been more and more attractive as a new
computing paradigm due to high flexibility for provisioning on-demand computing
resources that are used as services through the Internet. The issues around cloud service
discovery have considered by many researchers in the recent years. However,
in cloud computing, with the highly dynamic, distributed, the lack of standardized
description languages, diverse services offered at different levels and non-transparent
nature of cloud services, this research area has gained a significant attention. Robust
cloud service discovery approaches will assist the promotion and growth of cloud
service customers and providers, but will also provide a meaningful contribution to
the acceptance and development of cloud computing. In this dissertation, we have
proposed an automated cloud service discovery approach of cloud services. We have
also conducted extensive experiments to validate our proposed approach. The results
demonstrate the applicability of our approach and its capability of effectively identifying
and categorizing cloud services on the Internet. Firstly, we develop a novel
approach to build cloud service ontology. Cloud service ontology initially is built
based on the National Institute of Standards and Technology (NIST) cloud computing
standard. Then, we add new concepts to ontology by automatically analyzing real
cloud services based on cloud service ontology Algorithm. We also propose cloud
service categorization that use Term Frequency to weigh cloud service ontology concepts
and calculate cosine similarity to measure the similarity between cloud services.
The cloud service categorization algorithm is able to categorize cloud services to clusters for effective categorization of cloud services. In addition, we use Machine
Learning techniques to identify cloud service in real environment. Our cloud service
identifier is built by utilizing cloud service features extracted from the real cloud service
providers. We determine several features such as similarity function, semantic
ontology, cloud service description and cloud services components, to be used effectively
in identifying cloud service on the Web. Also, we build a unified model to
expose the cloud service’s features to a cloud service search user to ease the process of
searching and comparison among a large amount of cloud services by building cloud
service’s profile. Furthermore, we particularly develop a cloud service discovery Engine
that has capability to crawl the Web automatically and collect cloud services.
The collected datasets include meta-data of nearly 7,500 real-world cloud services
providers and nearly 15,000 services (2.45GB). The experimental results show that
our approach i) is able to effectively build automatic cloud service ontology, ii) is
robust in identifying cloud service in real environment and iii) is more scalable in
providing more details about cloud services.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 201
Ontologies in Cloud Computing - Review and Future Directions
Cloud computing as a technology has the capacity to enhance cooperation, scalability, accessibility, and offers discount prospects using improved and effective computing, and this capability helps organizations to stay focused. Ontologies are used to model knowledge. Once knowledge is modeled, knowledge management systems can be used to search, match, visualize knowledge, and also infer new knowledge. Ontologies use semantic analysis to define information within an environment with interconnecting relationships between heterogeneous sets. This paper aims to provide a comprehensive review of the existing literature on ontology in cloud computing and defines the state of the art. We applied the systematic literature review (SLR) approach and identified 400 articles; 58 of the articles were selected after further selection based on set selection criteria, and 35 articles were considered relevant to the study. The study shows that four predominant areas of cloud computing—cloud security, cloud interoperability, cloud resources and service description, and cloud services discovery and selection—have attracted the attention of researchers as dominant areas where cloud ontologies have made great impact. The proposed methods in the literature applied 30 ontologies in the cloud domain, and five of the methods are still practiced in the legacy computing environment. From the analysis, it was found that several challenges exist, including those related to the application of ontologies to enhance business operations in the cloud and multi-cloud. Based on this review, the study summarizes some unresolved challenges and possible future directions for cloud ontology researchers.publishedVersio
A Model-Driven Engineering Approach for ROS using Ontological Semantics
This paper presents a novel ontology-driven software engineering approach for
the development of industrial robotics control software. It introduces the
ReApp architecture that synthesizes model-driven engineering with semantic
technologies to facilitate the development and reuse of ROS-based components
and applications. In ReApp, we show how different ontological classification
systems for hardware, software, and capabilities help developers in discovering
suitable software components for their tasks and in applying them correctly.
The proposed model-driven tooling enables developers to work at higher
abstraction levels and fosters automatic code generation. It is underpinned by
ontologies to minimize discontinuities in the development workflow, with an
integrated development environment presenting a seamless interface to the user.
First results show the viability and synergy of the selected approach when
searching for or developing software with reuse in mind.Comment: Presented at DSLRob 2015 (arXiv:1601.00877), Stefan Zander, Georg
Heppner, Georg Neugschwandtner, Ramez Awad, Marc Essinger and Nadia Ahmed: A
Model-Driven Engineering Approach for ROS using Ontological Semantic
Towards Cleaning-up Open Data Portals: A Metadata Reconciliation Approach
This paper presents an approach for metadata reconciliation, curation and
linking for Open Governamental Data Portals (ODPs). ODPs have been lately the
standard solution for governments willing to put their public data available
for the society. Portal managers use several types of metadata to organize the
datasets, one of the most important ones being the tags. However, the tagging
process is subject to many problems, such as synonyms, ambiguity or
incoherence, among others. As our empiric analysis of ODPs shows, these issues
are currently prevalent in most ODPs and effectively hinders the reuse of Open
Data. In order to address these problems, we develop and implement an approach
for tag reconciliation in Open Data Portals, encompassing local actions related
to individual portals, and global actions for adding a semantic metadata layer
above individual portals. The local part aims to enhance the quality of tags in
a single portal, and the global part is meant to interlink ODPs by establishing
relations between tags.Comment: 8 pages,10 Figures - Under Revision for ICSC201
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The Interoperability of Things: Interoperable solutions as an enabler for IoT and Web 3.0
This paper presents an overview of the interoperability concepts along with the challenges for the IoT domain and the upcoming Web 3.0. We identify four levels of interoperability and the relevant solutions for accomplishing vertical and horizontal compatibility between the various layers of a modern IoT ecosystem, referred to as: technological, syntactic, semantic, and organizational interoperability. The goal is to achieve cross-domain interaction and facilitate the proper usage and management of the provided IoT services and applications. An interoperability framework is also proposed where the involved system components can cooperate and offer the seamless operation from the device to the backend framework. This by-design end-to-end interoperation enables the interplay of several complex service composition settings and the management of the system via patterns. The overall proposal is adopted by the EU funded project SEMIoTICS as an enabler towards the IoT and Web 3.0, even when products from different vendors are utilized
Adaptive learning-based resource management strategy in fog-to-cloud
Technology in the twenty-first century is rapidly developing and driving us into a new smart computing world, and emerging lots
of new computing architectures. Fog-to-Cloud (F2C) is among one of them, which emerges to ensure the commitment for
bringing the higher computing facilities near to the edge of the network and also help the large-scale computing system to be
more intelligent. As the F2C is in its infantile state, therefore one of the biggest challenges for this computing paradigm is to
efficiently manage the computing resources. Mainly, to address this challenge, in this work, we have given our sole interest for
designing the initial architectural framework to build a proper, adaptive and efficient resource management mechanism in F2C.
F2C has been proposed as a combined, coordinated and hierarchical computing platform, where a vast number of
heterogeneous computing devices are participating. Notably, their versatility creates a massive challenge for effectively handling
them. Even following any large-scale smart computing system, it can easily recognize that various kind of services is served for
different purposes. Significantly, every service corresponds with the various tasks, which have different resource requirements.
So, knowing the characteristics of participating devices and system offered services is giving advantages to build effective and
resource management mechanism in F2C-enabled system. Considering these facts, initially, we have given our intense focus for
identifying and defining the taxonomic model for all the participating devices and system involved services-tasks.
In any F2C-enabled system consists of a large number of small Internet-of-Things (IoTs) and generating a continuous and
colossal amount of sensing-data by capturing various environmental events. Notably, this sensing-data is one of the key
ingredients for various smart services which have been offered by the F2C-enabled system. Besides that, resource statistical
information is also playing a crucial role, for efficiently providing the services among the system consumers. Continuous
monitoring of participating devices generates a massive amount of resource statistical information in the F2C-enabled system.
Notably, having this information, it becomes much easier to know the device's availability and suitability for executing some tasks
to offer some services. Therefore, ensuring better service facilities for any latency-sensitive services, it is essential to securely
distribute the sensing-data and resource statistical information over the network. Considering these matters, we also proposed
and designed a secure and distributed database framework for effectively and securely distribute the data over the network.
To build an advanced and smarter system is necessarily required an effective mechanism for the utilization of system resources.
Typically, the utilization and resource handling process mainly depend on the resource selection and allocation mechanism. The
prediction of resources (e.g., RAM, CPU, Disk, etc.) usage and performance (i.e., in terms of task execution time) helps the
selection and allocation process. Thus, adopting the machine learning (ML) techniques is much more useful for designing an
advanced and sophisticated resource allocation mechanism in the F2C-enabled system. Adopting and performing the ML
techniques in F2C-enabled system is a challenging task. Especially, the overall diversification and many other issues pose a
massive challenge for successfully performing the ML techniques in any F2C-enabled system. Therefore, we have proposed and
designed two different possible architectural schemas for performing the ML techniques in the F2C-enabled system to achieve
an adaptive, advance and sophisticated resource management mechanism in the F2C-enabled system. Our proposals are the
initial footmarks for designing the overall architectural framework for resource management mechanism in F2C-enabled system.La tecnologia del segle XXI avança ràpidament i ens condueix cap a un nou món intel·ligent, creant nous models d'arquitectures informàtiques. Fog-to-Cloud (F2C) és un d’ells, i sorgeix per garantir el compromís d’acostar les instal·lacions informàtiques a prop de la xarxa i també ajudar el sistema informàtic a gran escala a ser més intel·ligent. Com que el F2C es troba en un estat preliminar, un dels majors reptes d’aquest paradigma tecnològic és gestionar eficientment els recursos informàtics. Per fer front a aquest repte, en aquest treball hem centrat el nostre interès en dissenyar un marc arquitectònic per construir un mecanisme de gestió de recursos adequat, adaptatiu i eficient a F2C.F2C ha estat concebut com una plataforma informàtica combinada, coordinada i jeràrquica, on participen un gran nombre de dispositius heterogenis. La seva versatilitat planteja un gran repte per gestionar-los de manera eficaç. Els serveis que s'hi executen consten de diverses tasques, que tenen requisits de recursos diferents. Per tant, conèixer les característiques dels dispositius participants i dels serveis que ofereix el sistema és un requisit per dissenyar mecanismes eficaços i de gestió de recursos en un sistema habilitat per F2C. Tenint en compte aquests fets, inicialment ens hem centrat en identificar i definir el model taxonòmic per a tots els dispositius i sistemes implicats en l'execució de tasques de serveis. Qualsevol sistema habilitat per F2C inclou en un gran nombre de dispositius petits i connectats (conegut com a Internet of Things, o IoT) que generen una quantitat contínua i colossal de dades de detecció capturant diversos events ambientals. Aquestes dades són un dels ingredients clau per a diversos serveis intel·ligents que ofereix F2C. A més, el seguiment continu dels dispositius participants genera igualment una gran quantitat d'informació estadística. En particular, en tenir aquesta informació, es fa molt més fàcil conèixer la disponibilitat i la idoneïtat dels dispositius per executar algunes tasques i oferir alguns serveis. Per tant, per garantir millors serveis sensibles a la latència, és essencial distribuir de manera equilibrada i segura la informació estadística per la xarxa. Tenint en compte aquests assumptes, també hem proposat i dissenyat un entorn de base de dades segura i distribuïda per gestionar de manera eficaç i segura les dades a la xarxa. Per construir un sistema avançat i intel·ligent es necessita un mecanisme eficaç per a la gestió de l'ús dels recursos del sistema. Normalment, el procés d’utilització i manipulació de recursos depèn principalment del mecanisme de selecció i assignació de recursos. La predicció de l’ús i el rendiment de recursos (per exemple, RAM, CPU, disc, etc.) en termes de temps d’execució de tasques ajuda al procés de selecció i assignació. Adoptar les tècniques d’aprenentatge automàtic (conegut com a Machine Learning, o ML) és molt útil per dissenyar un mecanisme d’assignació de recursos avançat i sofisticat en el sistema habilitat per F2C. L’adopció i la realització de tècniques de ML en un sistema F2C és una tasca complexa. Especialment, la diversificació general i molts altres problemes plantegen un gran repte per realitzar amb èxit les tècniques de ML. Per tant, en aquesta recerca hem proposat i dissenyat dos possibles esquemes arquitectònics diferents per realitzar tècniques de ML en el sistema habilitat per F2C per aconseguir un mecanisme de gestió de recursos adaptatiu, avançat i sofisticat en un sistema F2C. Les nostres propostes són els primers passos per dissenyar un marc arquitectònic general per al mecanisme de gestió de recursos en un sistema habilitat per F2C.Postprint (published version
CloudOps: Towards the Operationalization of the Cloud Continuum: Concepts, Challenges and a Reference Framework
The current trend of developing highly distributed, context aware, heterogeneous computing intense and data-sensitive applications is changing the boundaries of cloud computing. Encouraged by the growing IoT paradigm and with flexible edge devices available, an ecosystem of a combination of resources, ranging from high density compute and storage to very lightweight embedded computers running on batteries or solar power, is available for DevOps teams from what is known as the Cloud Continuum. In this dynamic context, manageability is key, as well as controlled operations and resources monitoring for handling anomalies. Unfortunately, the operation and management of such heterogeneous computing environments (including edge, cloud and network services) is complex and operators face challenges such as the continuous optimization and autonomous (re-)deployment of context-aware stateless and stateful applications where, however, they must ensure service continuity while anticipating potential failures in the underlying infrastructure. In this paper, we propose a novel CloudOps workflow (extending the traditional DevOps pipeline), proposing techniques and methods for applications’ operators to fully embrace the possibilities of the Cloud Continuum. Our approach will support DevOps teams in the operationalization of the Cloud Continuum. Secondly, we provide an extensive explanation of the scope, possibilities and future of the CloudOps.This research was funded by the European project PIACERE (Horizon 2020 Research and Innovation Programme, under grant agreement No. 101000162)
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