A systematic mapping study of performance analysis and modelling of cloud systems and applications

Odun-Ayo, Isaac; Williams, Toro-Abasi; Odusami, Modupe; Yahaya, Jamaiah

doi:10.11591/ijece.v11i2.pp1839-1848

Cited by 5 publications

(5 citation statements)

References 23 publications

(32 reference statements)

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“…The method was carried out automatically using the database's sorting function. The research question produced in the preceding procedure served as the basis for the selection standard [39], [40]. Then, the publications were collected between 2017 and 2021, and only English-language published articles were chosen from IEEE, Scopus, and WOS.…”

Section: Screeningmentioning

confidence: 99%

Systematic literature review: application of deep learning processing technique for fig fruit detection and counting

et al. 2023

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Deep learning has shown much promise in target identification in recent years, and it's becoming more popular in agriculture, where fig fruit detection and counting have become important. In this study, a systematic literature review (SLR) is utilised to evaluate a deep learning algorithm for detecting and counting fig fruits. The SLR is based on the widely used 'Reporting Standards for Systematic Evidence Synthetics' (ROSES) review process. The study starts by formulating the research questions, and the proposed SLR approach is critically discussed until the data abstraction and analysis process is completed. Following that, 33 relevant research involving the agriculture sector, fruit, were selected from many studies. IEEE, Scopus, and Web of Sciences are three databases to investigate. Due to the lack of fig fruit research, fruit and vegetable studies have been included because they use similar methods and processes. The SLR found that various deep learning algorithms can count fig fruit in the field. Furthermore, as most approaches obtained acceptable results, deep learning's performance is acceptable in F1-score and average precision (AP), higher than 80%. Moreover, improvements can be produced by enhancing the existing deep learning model with the personal dataset.

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Section: Screeningmentioning

confidence: 99%

Systematic literature review: application of deep learning processing technique for fig fruit detection and counting

et al. 2023

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“…Cloud computing displays several benefits, such as dynamic environment, on-demand services, scalability, yet additionally has several challenges that ought to be considered by experts such as privacy and security, virtualization, scheduling, resource discovery, reducing the attacks, service discovery, data replication, service recommendation, service composition, and selection. Numerous businesses, including Amazon, IBM, Microsoft, and Google offer several services for cloud computing [1], [2]. Subsequently, cloud computing offers a pool of on-demand computing resources to users to find their needed service(s) that fulfill their request.…”

Section: Introductionmentioning

confidence: 99%

“…The foremost critical objective for users is to effectively utilize their requested services for lower operational cost, increasing scalability, improving performance, and utilizing their resources in efficient ways. Subsequently, cloud computing has developed to meet users' computing prerequisites by advertising new features designed to enhance the sharing of computing resources and applications on demand and based on pay-as-you-go through the internet [2], [6].…”

Section: Introductionmentioning

confidence: 99%

Location-aware deep learning-based framework for optimizing cloud consumer quality of service-based service composition

Mohammed

Soliman²,

Omara³

2023

IJECE

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<span>The expanding propensity of organization users to utilize cloud services urges to deliver services in a service pool with a variety of functional and non-functional attributes from online service providers. brokers of cloud services must intense rivalry competing with one another to provide quality of service (QoS) enhancements. Such rivalry prompts a troublesome and muddled providing composite services on the cloud using a simple service selection and composition approach. Therefore, cloud composition is considered a non-deterministic polynomial (NP-hard) and economically motivated problem. Hence, developing a reliable economic model for composition is of tremendous interest and to have importance for the cloud consumer. This paper provides “A location-aware deep learning framework for improving the QoS-based service composition for cloud consumers”. The proposed framework is firstly reducing the dimensions of data. Secondly, it applies a combination of the deep learning long short-term memory network and particle swarm optimization algorithm additionally to considering the location parameter to correctly forecast the QoS provisioned values. Finally, it composes the ideal services need to reduce the customer cost function. The suggested framework's performance has been demonstrated using a real dataset, proving that it superior the current models in terms of prediction and composition accuracy.</span>

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“…In distributed algorithm, the work load is harmonized 82 between all the nodes collectively. It can be applied in two forms: cooperative that relies on each node to reach the goal, and non-cooperative form that balances the load on each node independently [12]. Whereas, non-distributed algorithm, balance the load by one node or node-set.…”

Section: Introductionmentioning

confidence: 99%

Optimized load balance scheduling algorithm

Abdul-Hussein

Mohammed

2022

TELKOMNIKA

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A systematic mapping study of performance analysis and modelling of cloud systems and applications

Cited by 5 publications

References 23 publications

Systematic literature review: application of deep learning processing technique for fig fruit detection and counting

Systematic literature review: application of deep learning processing technique for fig fruit detection and counting

Location-aware deep learning-based framework for optimizing cloud consumer quality of service-based service composition

Optimized load balance scheduling algorithm

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