Modeling Smart Cloud Computing Resource Allocation in E-Learning

Alhakkak, Nada M.

doi:10.1109/cvidl51233.2020.00-87

Cited by 5 publications

(5 citation statements)

References 11 publications

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“…[125], [126], [127], [128], [129], [130], [131], [132], [133], [134], [135], [136], [137], [138] , [139], [140] Privet cloud Private cloud 18 12%…”

Section: Figure 12 Distribution Of Papers Discussing the Effect Of E-...mentioning

confidence: 99%

“…[157], [158], [159], [160] , [161] [21], [157], [8], [26], [27], [158], [29], [32], [33], [5], [34], [35], [36], [38], [40], [41 ], [42], [43], [44], [45], [125], [49], [126], [127], [128], [129], [1], [51], [52], [53], [5 4], [55], [56], [58], [59], [60], [61], [62], [63], [64], [65], [65], [68], [70], [4], [72], [7 3],…”

Section: Figure 12 Distribution Of Papers Discussing the Effect Of E-...mentioning

confidence: 99%

“…[98], [101], [102], [104], [162], [130], [110], [112], [113], [114], [115], [116], [119] Performance 27 18% [34], [38], [40], [49], [50], [129], [160], [64], [65], [65], [75], [82], [105], [106], [132], [133], [107], [111], [117], [122], [123], [149], [137], [150], [151], [138], [120] Virtual 10 7% [23], [159], [147], [50], [118], [120], [152], [153], [154], [50], …”

Section: E-learning Effectmentioning

confidence: 99%

See 2 more Smart Citations

E-Learning-Based Cloud Computing Environment: A Systematic Review, Challenges, and Opportunities

Eljak,

Ibrahim,

Saeed

et al. 2024

IEEE Access

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New technologies drive educational shifts, transforming offline to online learning. This study investigates e-learning and cloud computing integration to understand synergies and their potential impact. The study addresses two primary research questions: the influence of elearning on factors like architecture, software, performance, security, hardware, network, and virtual aspects, and the examination of cloud computing services and models such as SaaS, PaaS, IaaS, and S.O.A. The research aims to provide insights into how e-learning is incorporated in a cloud computing environment. The motivation behind this study is to investigate the intricate relationship between e-learning and cloud computing. By analyzing 154 scientific papers, the study delves into the specifics of this integration, highlighting trends and areas that have received more attention. The study examines e-learning in a cloud computing environment, focusing on architecture (27%), general topics (21%), software (19%), and performance (18%). Virtual environments have fewer security issues, while storage and network focus are more prevalent. Cloud computing services are mainly all services, with software as a service (18%), infrastructure as a service (17%), and platforms as a service (10%). Most studies are based on public clouds (74%), all other models (11%), and hybrid clouds (3%). The study examines elearning integration in cloud computing, highlighting limitations in hybrid and private clouds, specialized infrastructure, and a gap in platforms and infrastructure offerings.

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“…[125], [126], [127], [128], [129], [130], [131], [132], [133], [134], [135], [136], [137], [138] , [139], [140] Privet cloud Private cloud 18 12%…”

Section: Figure 12 Distribution Of Papers Discussing the Effect Of E-...mentioning

confidence: 99%

Section: Figure 12 Distribution Of Papers Discussing the Effect Of E-...mentioning

confidence: 99%

Section: E-learning Effectmentioning

confidence: 99%

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E-Learning-Based Cloud Computing Environment: A Systematic Review, Challenges, and Opportunities

Eljak,

Ibrahim,

Saeed

et al. 2024

IEEE Access

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“…They have validated and illustrated the appropriateness of suggested methodology for assisting cloud providers in reducing potential service violations and penalties through experiments. Alhakkak [9] proposed a smart resource allocation method for the systems of e-learning in a cloud computing environment; it used the ABC approach, which is part of the swarm intelligence family. The suggested approach assigned resources based upon each resource's assessed probability value.…”

Section: Introductionmentioning

confidence: 99%

Cloud resources modelling using smart cloud management

2022

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Cloud computing complexity is growing rapidly with the advancements that it is witnessing. It has created a requirement to simplify the process of configuring cloud and re-configuring it when required, it also involves tasks like auto scaling of infrastructure, elastic computing and maintaining the health of the servers. The proposed method introduces a smart cloud management using knowledge base, which models the resources of cloud; it handles service level agreement and its evaluations. The proposed knowledge base supports representational state transfer (REST/RESTful) services to store and manipulate different cloud aspects like type of application, business configuration, and metrics value and its type; it also implements the strategy for efficient resource management for smart clouds. The proposed architecture consists of smart cloud engine (which provides autonomous services, which help to exploit cloud resources for service optimization and to perform service automation), knowledge base (KB) (provide a cloud ontology which will help in the management of resources and provides intelligence to the smart cloud), server and cloud enrolment, designated monitoring tool and moderator. The resulted module is easy to integrate with any of the existing cloud management tool or orchestrator. As It is developed using REST protocol and extensible markup language (XML) language it is also easy to integrate with existing monitoring tool or application programming interface (APIs).

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“…Learning needs and learning preferences are not static. With the improvement of their abilities, students will gradually ask for more difficult learning resources [15][16][17][18][19]. Therefore, the recommendation methods for online course learning resources should not only consider the short-term needs of students, but also take account of the changes of their learning status.…”

Section: Introductionmentioning

confidence: 99%

Online Course Learning Resource Recommendation Based on Difficulty Matching

Jia¹

2022

Int. J. Emerg. Technol. Learn.

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The current methods for online course learning resource recommendation tend to continuously push similar items to students. To make students more satisfied with the overall recommendation service, it is necessary to explore the recommendation methods for online course learning resources based on difficulty matching. Therefore, this paper devises an online course learning resource recommendation method based on difficulty matching. Firstly, an online course learning resource recommendation approach was developed based on the autoencoder, in the light of difficulty matching. The flowchart of learning resource recommendation was presented, and the proposed algorithm was described in details. Then, the authors depicted the flow of the online course learning resource recommendation model, which considers difficulty matching, and proposed to capture the hierarchy of resources by assigning difficulty labels to texts, such that the proposed recommendation model outputs interpretable recommendation results. Finally, the authors designed a model that can discover the influence of the difficulty of learned resources on the difficulty of unlearned learning resources, and recommended online course learning resources considering difficulty matching. The effectiveness of the proposed model is verified through experiments.

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Modeling Smart Cloud Computing Resource Allocation in E-Learning

Cited by 5 publications

References 11 publications

E-Learning-Based Cloud Computing Environment: A Systematic Review, Challenges, and Opportunities

E-Learning-Based Cloud Computing Environment: A Systematic Review, Challenges, and Opportunities

Cloud resources modelling using smart cloud management

Online Course Learning Resource Recommendation Based on Difficulty Matching

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