RETRACTED ARTICLE: Cloud service recommendation system based on clustering trust measures in multi-cloud environment

Priya, A. Shenbaga Bharatha; Bhuvaneswaran, R. S.

doi:10.1007/s12652-020-02368-2

Cited by 11 publications

(3 citation statements)

References 24 publications

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“…Cloud Service Selection, a primary concern where N number of services being deployed with many similar matching characteristic that needs to be addressed for the benefits of cloud consumers in a long run. Most perplexing task to find the reliable service from cloud server, hence cloud service recommendation is proposed [21,16] using clustering based on trust degree computation algorithm and service suggestion is given respectively. AHP techniques ensures the weights of user requirements, many researchers implemented for ranking the services as well [1, 7, 8,…”

Section: Related Workmentioning

confidence: 99%

“…Most perplexing task to find the reliable service from cloud server, hence cloud service recommendation is proposed [21,16] using clustering based on trust degree computation algorithm and service suggestion is given respectively. AHP techniques ensures the weights of user requirements, many researchers implemented for ranking the services as well [1,7,8,11,12,19].…”

Section: Related Workmentioning

confidence: 99%

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C-DRM: Coalesced P-TOPSIS Entropy Technique addressing Uncertainty in Cloud Service Selection

Nivitha¹,

Pabitha²

2022

ITC

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Cloud Computing is diversified with its services exponentially and lured large number of consumers towards the technology indefinitely. It has become a highly challenging problem to satiate the user requirements. Most of the existing system ingest large search space or provide inappropriate service; hence, there is a need for the reliable and space competent service selection/ranking in the cloud environment. The proposed work introduces a novel pruning method and Dual Ranking Method (DRM) to rank the services from n services in terms of space conserving and providing reliable service quenching the user requirements as well. Dual Ranking Method (DRM) is proposed focusing on the uncertainty of user preferences along with their priorities; converting it to weights with the use of Jensen-Shannon (JS) Entropy Function. The ranking of service is employed through Priority-Technique for Order of Preference by Similarity to Ideal Solution (P-TOPSIS) and space complexity is reduced by novel Utility Pruning method. The performance of the proposed work Clustering – Dual Ranking Method (C-DRM) is estimated in terms of accuracy, Closeness Index (CI) and space complexity have been validated through case study where results outperforms the existing approaches

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

C-DRM: Coalesced P-TOPSIS Entropy Technique addressing Uncertainty in Cloud Service Selection

Nivitha¹,

Pabitha²

2022

ITC

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“…Whereas, the cyber subsystem of the proposed framework, initially at the fog layer classifies the panic health status (PHS) of the stranded individuals in real‐time based on the acquired health data and analyzes the novelty of the data for avoiding unnecessary data traffic to Cloud. Priya et al 24 present a cloud service recommendation system using clustering based trust degree computation algorithm. The service efficiently allocates services to the end user using trustworthy service allocation algorithm.…”

Section: Related Workmentioning

confidence: 99%

A cloud‐based service for emergency evacuation of mine water inrush accidents

Yao

et al. 2020

Concurrency and Computation

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Summary In a mine water inrush accident, a fast and accurate emergency evacuation response is essential for ensuring the miner's safety. Conventional emergency evacuation response in the mining industry lacks integration of data from monitoring systems and adjustment of the evacuation plan. In this study, a cloud‐based service, named the Cloud EE‐MWI, is proposed for the emergency evacuation for water inrush accidents to support managers in making the best decisions. The Cloud EE‐MWI's architecture consists of three layers, including the information, computation, and visualization layers. Information layer acquires data from monitoring systems and processes the collected data with pre‐saved basic information of the mine to generate a time‐varying network of the accident evolution. The computation layer constructs an workflow to find a reliable water‐avoiding route. The latest departure route algorithm is proposed and the time‐varying earliest arrival algorithm is implemented in parallel on cloud servers to boost the evacuation path planning. Finally, the visualization layer focuses on displaying data from sensors and obtained results from the computation layer on the web‐side. A real case study is investigated to demonstrate that the service can integrate the information of the monitoring systems and support the useful solution of the emergency evacuation.

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RETRACTED ARTICLE: Bio-inspired approach and integrity check mechanism for secure data storage in multi-cloud environment

Mohammed

2021

J Ambient Intell Human Comput

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RETRACTED ARTICLE: Cloud service recommendation system based on clustering trust measures in multi-cloud environment

Cited by 11 publications

References 24 publications

C-DRM: Coalesced P-TOPSIS Entropy Technique addressing Uncertainty in Cloud Service Selection

C-DRM: Coalesced P-TOPSIS Entropy Technique addressing Uncertainty in Cloud Service Selection

A cloud‐based service for emergency evacuation of mine water inrush accidents

RETRACTED ARTICLE: Bio-inspired approach and integrity check mechanism for secure data storage in multi-cloud environment

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