Foundations and Technological Landscape of Cloud Computing

Phaphoom, Nattakarn; Abrahamsson, Pekka

doi:10.1155/2013/782174

Cited by 23 publications

(7 citation statements)

References 48 publications

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“…Regarding future works, a new alternative is being explored regarding data storage thanks to the increasing number of cloud and Big Data solutions [75]. To this end, a review of Big Data solutions is required to identify the best way to make HYBINT consistent with both cloud and NoSQL's alternatives [76,77].…”

Section: Final Notes and Conclusionmentioning

confidence: 99%

HYBINT: A Hybrid Intelligence System for Critical Infrastructures Protection

Hingant

Zambrano

Pérez

et al. 2018

Security and Communication Networks

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Cyberattacks, which consist of exploiting security vulnerabilities of computer networks and systems for any kind of malicious purpose (e.g., extortion, data steal, assets hijacking), have been continuously increasing worldwide in recent years. Cyberspace appears today as a new battlefield, along with physical world scenarios (land, sea, air, and space), for the organizations defence and security. Besides, by the fact that attacks from the physical world may have significant implications in the cyber world and vice versa, these dimensions cannot be understood independently. However, the most common intelligence systems offer an insufficient situational awareness exclusively focused on one of these decision spaces. This article introduces HYBINT, an enhanced intelligence system that provides the necessary decision-making support for an efficient critical infrastructures protection by combining the real-time situation of the physical and cyber domains in a single visualization space. HYBINT is a real cross-platform solution which supplies, through Big Data analytical methods and advanced representation techniques, hybrid intelligence information from significant data of both physical and cyber data sources in order to bring an adequate hybrid situational awareness (HSA) of the cyber-physical environment. The proposal will be validated in a detailed scenario in which HYBINT system will be evaluated.

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Section: Final Notes and Conclusionmentioning

confidence: 99%

HYBINT: A Hybrid Intelligence System for Critical Infrastructures Protection

Hingant

Zambrano

Pérez

et al. 2018

Security and Communication Networks

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show abstract

“…In its inception, cloud computing revolved around virtualization as main resource compartmentalization or consolidation strategy [63,85] to support application isolation and platform customization to suit user needs [17,18], as well as to enable pooling and dynamically assigning computing resources from clusters of servers [147]. The significant performance improvement and overhead reduction of virtualization technology [81] propelled its adoption as key delivery technology in cloud computing [24].…”

Section: Concepts and Definitionsmentioning

confidence: 99%

Cloud resource management: towards efficient execution of large-scale scientific applications and workflows on complex infrastructures

2017

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Cloud computing evolved from the concept of utility computing, which is defined as the provision of computational and storage resources as a metered service. Another key characteristic of cloud computing is multitenancy, which enables resource and cost sharing among a large pool of users. Characteristics such as multitenancy and elasticity perfectly fit the requirements of modern data-intensive research and scientific endeavors. In parallel, as science relies on the analysis of very large data sets, data management and processing must be performed in a scalable and automated way. Workflows have emerged as a way to formalize and structure data analysis, thus becoming an increasingly popular paradigm for scientists to handle complex scientific processes. One of the key enablers of this conjunction of cloud computing and scientific workflows is resource management. However, several issues related to data-intensive loads, complex infrastructures such as hybrid and multicloud environments to support large-scale execution of workflows, performance fluctuations, and reliability, pose as challenges to truly position clouds as viable high-performance infrastructures for scientific computing. This paper presents a survey on cloud resource management that provides an extensive study of the field. A taxonomy is proposed to analyze the selected works and the analysis ultimately leads to the definition of gaps and future challenges to be addressed by research and development.

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“…The aim of the cloud computing model is to increase the opportunities for cloud user by accessing leased infrastructure and software applications from anywhere anytime manner. Therefore, cloud computing offers a new kind of information and services that widen the new vision of information technology (IT) services [ 2 , 3 ]. The recent hype of the cloud computing and at the same time the rise of the smart mobile devices help us to envision mobile cloud computing (MCC).…”

Section: Introductionmentioning

confidence: 99%

Cloud Service Selection Using Multicriteria Decision Analysis

Whaiduzzaman

Gani

Anuar

et al. 2014

The Scientific World Journal

151

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Cloud computing (CC) has recently been receiving tremendous attention from the IT industry and academic researchers. CC leverages its unique services to cloud customers in a pay-as-you-go, anytime, anywhere manner. Cloud services provide dynamically scalable services through the Internet on demand. Therefore, service provisioning plays a key role in CC. The cloud customer must be able to select appropriate services according to his or her needs. Several approaches have been proposed to solve the service selection problem, including multicriteria decision analysis (MCDA). MCDA enables the user to choose from among a number of available choices. In this paper, we analyze the application of MCDA to service selection in CC. We identify and synthesize several MCDA techniques and provide a comprehensive analysis of this technology for general readers. In addition, we present a taxonomy derived from a survey of the current literature. Finally, we highlight several state-of-the-art practical aspects of MCDA implementation in cloud computing service selection. The contributions of this study are four-fold: (a) focusing on the state-of-the-art MCDA techniques, (b) highlighting the comparative analysis and suitability of several MCDA methods, (c) presenting a taxonomy through extensive literature review, and (d) analyzing and summarizing the cloud computing service selections in different scenarios.

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Foundations and Technological Landscape of Cloud Computing

Cited by 23 publications

References 48 publications

HYBINT: A Hybrid Intelligence System for Critical Infrastructures Protection

HYBINT: A Hybrid Intelligence System for Critical Infrastructures Protection

Cloud resource management: towards efficient execution of large-scale scientific applications and workflows on complex infrastructures

Cloud Service Selection Using Multicriteria Decision Analysis

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