An ontology for heterogeneous resources management interoperability and HPC in the cloud

Castañé, Gabriel G.; Xiong, Huanhuan; Dong, Dapeng; Morrison, John P.

doi:10.1016/j.future.2018.05.086

Cited by 28 publications

(12 citation statements)

References 18 publications

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“…It also automates the process of executing users' tasks, resulting in improvements to users' efficiency, information exchange and productivity. Similarly, Castane et al (2018) proposed a semantically enabled architecture for managing heterogenous resources within a multi-cloud environment; delivering high-performance computing and optimising cloud resources utilisation. The architecture leverages mOSAIC ontology and extends it to foster selfmanagement of services within the cloud.…”

Section: Discovery Selection and Utilisation Of Cloud Servicesmentioning

confidence: 99%

Leveraging cloud computing for the semantic web: review and trends

et al. 2019

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Semantic and Cloud Computing technologies have become vital elements for developing and deploying solutions across diverse fields in computing. While they are independent of each other, they can be integrated in diverse ways for developing solutions and this has been significantly explored in recent times. With the migration of web-based data and applications to cloud platforms and the evolution of the web itself from a social, web 2.0 to a semantic, web 3.0 comes the convergence of both technologies. While several concepts and implementations have been provided regarding interactions between the two technologies from existing research, without an explicit classification of the modes of interaction, it can be quite challenging to articulate the interaction modes, hence building upon them can be a very daunting task. Hence, this research identifies and describes the modes of interaction between them. Furthermore, a "cloud-driven" interaction mode which focuses on fully maximising cloud computing characteristics and benefits for driving the semantic web is described; providing an approach for evolving the semantic web and delivering automated semantic annotation on a large-scale to web applications.

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Section: Discovery Selection and Utilisation Of Cloud Servicesmentioning

confidence: 99%

Leveraging cloud computing for the semantic web: review and trends

et al. 2019

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“…Ultimately, outcomes gotten through current effort must be protracted for employee expertise -already available on the SFO -to analyze association working out actions then presentation outcomes for dexterity obligatory through company. Integration tests of the technologies which used have been carried out using real industrial data showing the feasibility and robustness of the proposed solution [33]. An architecture was proposed on behalf of subsidiary energetic combination routes for physical fast-tracks for resources of clouding material, plus management of ontology which grounded on resources.…”

Section: Discussionmentioning

confidence: 99%

“…In 2018, Castañé et al [33] an architecture was proposed on behalf of subsidiary energetic combination routes for physical fast-tracks for resources of clouding material, plus management of ontology which grounded on resources. This construction obtainable for scheme of Cloud Lightning for demonstration in what way Ontology of cloud lightning which applied for management of autonomous resource.…”

Section: Literature Reviewmentioning

confidence: 99%

Unified Ontology Implementation of Cloud Computing for Distributed Systems

Ageed

Ibrahim

Sadeeq

2020

CJAST

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The ability to provide massive data storage, applications, platforms plus many other services leads to make the number of clouds services providers been increased. Providing different types of services and resources by various providers implies to get a high level of complexity. This complexity leads to face many challenges related to security, reliability, discovery, service selection, and interoperability. In this review, we focus on the use of many technologies and methods for utilizing the semantic web and ontology in cloud computing and distributed system as a solution for these challenges. Cloud computing does not have an own search engine to satisfy the needs of the providers of the cloud service. Using ontology enhances the cloud computing self-motivated via an intelligent framework of SaaS and consolidating the security by providing resources access control. The use RDF and OWL semantic technologies in the modeling of a multi-agent system are very effective in increases coordination the interoperability. One of the most efficient proposed frameworks is building cloud computing marketplace that collects the consumer's requirements of cloud services provider and managing these needs and resources to provide quick and reliable services.

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“…Castane et al [22] illustrated mOSAIC ontology for inter-cloud interoperability which was extended to create the cloud lightning ontology (CL-ontology). CL-ontology was the incorporation of heterogeneous resources and high performance computing (HPC) environments in the cloud.…”

Section: Literature Reviewmentioning

confidence: 99%

An Efficient Applications Cloud Interoperability Framework Using I-Anfis

Ramalingam¹,

Mohan

2021

Symmetry

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Cloud interoperability provides cloud services such as Software as a Service (SaaS) or customer system to communicate between the cloud providers. However, one of the most important barriers for existing researches was to adopt the application’s or data’s in cloud computing environments so as to obtain efficient cloud interoperability. This paper focuses on reliable cloud interoperability with a heterogeneous cloud computing resource environment with the objective of providing unilateral provision computing capabilities of a cloud server without the help of human interaction and allowing proper utilization of applications and services across various domains by using an effective cloud environment available at runtime. Moreover, the framework uses hybrid squirrel search genetic algorithm (HSSGA) to select the relevant features from a set of extracted features in order to eliminate irrelevant data which provides advantages of low computational time and less memory usage. Thereafter, for a proper selection of cloud server with respect to the selected features, the system has developed the improved adaptive neuro-fuzzy inference system (I-ANFIS) which provides accurate server selection and helps against uncertainties caused by servers or applications. Hence, the experimental result of the proposed framework gives an accuracy of 94.24% and remains more efficient compared to existing frameworks.

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An ontology for heterogeneous resources management interoperability and HPC in the cloud

Cited by 28 publications

References 18 publications

Leveraging cloud computing for the semantic web: review and trends

Leveraging cloud computing for the semantic web: review and trends

Unified Ontology Implementation of Cloud Computing for Distributed Systems

An Efficient Applications Cloud Interoperability Framework Using I-Anfis

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