A Fuzzy Logic-Based Method for Replica Placement in the Peer to Peer Cloud Using an Optimization Algorithm

Mohammadi, Behnaz; Navimipour, Nima Jafari

doi:10.1007/s11277-021-08936-9

Cited by 11 publications

(7 citation statements)

References 79 publications

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“…Use the hybrid fuzzy logic and ant colony optimization algorithm to reduce users' waiting time and discover the most suitable and optimal nodes for placement data replication. The proposed algorithm outperformed the other algorithms [38].…”

Section: Related Workmentioning

confidence: 87%

A novel hybrid arithmetic optimization algorithm and salp swarm algorithm for data placement in cloud computing

et al. 2023

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In recent years, the Internet of Things (IoT) has led to the spread of cloud computing devices in all commercial, industrial and agricultural sectors. The use of cloud computing environment services is increasing exponentially with all technology applications based on IoT. Fog computing has led to addressing issues in cloud computing environments. Fog computing reduces load balancing, processing, bandwidth, and storage as data file replication from the cloud to the network closest to sensors in different geographic locations. Traditional cloud computing leads to an increase in response time and processing time, and processing in the performance of data replication. We need replication strategies to meet users' requirements across different geographic locations while effectively harnessing fog computing capabilities to optimally select and place data replication of IoT services on cloud resources. In this strategy, the identification and mode of the data replication problem are designed as a multi-objective optimization problem that considers the heterogeneity of resources, least cost path, distance, and applications based on replication requirements. Firstly, a new hybrid metaheuristic method, using the Arithmetic Optimization Algorithm (AOA) and the salp swarm algorithm (SSA), is proposed to handle the problem of selection and placement data replication in fog computing. This strategy, called AOASSA, depends on using fog computing to optimally select and place data replication of IoT services on cloud resources. Secondly, the Floyd algorithm is used to strategy the least cost path, distance, and data transmission in different geographic locations. To validate the AOASSA strategy a set of experiments was carried out to validate the proposed strategy AOASSA. The performance of AOASSA is tested and compared with other swarm intelligence. Experiment results show the superiority of AOASSA over its competitors in terms of performance measures, such as least cost path, distance, and bandwidth.

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Section: Related Workmentioning

confidence: 87%

A novel hybrid arithmetic optimization algorithm and salp swarm algorithm for data placement in cloud computing

et al. 2023

View full text Add to dashboard Cite

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“…Reduce user wait times by utilizing the hybrid fuzzy logic and ant colony optimization technique to identify the most appropriate and effective nodes for placement data replication. The suggested algorithm fared better than the competition [ 38 ]. An overview of the given studies is presented in Table 1 .…”

Section: Related Workmentioning

confidence: 99%

AOEHO: A New Hybrid Data Replication Method in Fog Computing for IoT Application

Mohamed

Abualigah

Alburaikan

et al. 2023

Sensors

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Recently, the concept of the internet of things and its services has emerged with cloud computing. Cloud computing is a modern technology for dealing with big data to perform specified operations. The cloud addresses the problem of selecting and placing iterations across nodes in fog computing. Previous studies focused on original swarm intelligent and mathematical models; thus, we proposed a novel hybrid method based on two modern metaheuristic algorithms. This paper combined the Aquila Optimizer (AO) algorithm with the elephant herding optimization (EHO) for solving dynamic data replication problems in the fog computing environment. In the proposed method, we present a set of objectives that determine data transmission paths, choose the least cost path, reduce network bottlenecks, bandwidth, balance, and speed data transfer rates between nodes in cloud computing. A hybrid method, AOEHO, addresses the optimal and least expensive path, determines the best replication via cloud computing, and determines optimal nodes to select and place data replication near users. Moreover, we developed a multi-objective optimization based on the proposed AOEHO to decrease the bandwidth and enhance load balancing and cloud throughput. The proposed method is evaluated based on data replication using seven criteria. These criteria are data replication access, distance, costs, availability, SBER, popularity, and the Floyd algorithm. The experimental results show the superiority of the proposed AOEHO strategy performance over other algorithms, such as bandwidth, distance, load balancing, data transmission, and least cost path.

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“…The big data has many security challenges and it is increased when it is related to the IoT medical data. Big data management security is the major issue in cloud computing, which usually focuses on data classification and encryption mechanisms [162,163]. It occupies more memory space to store the medical data, which is not cost-effective.…”

Section: Open Issues and Future Workmentioning

confidence: 99%

The Roles of Cloud-Based Systems on the Cancer-Related Studies: A Systematic Literature Review

Zhou

2022

IEEE Access

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The advances in Wireless-based technologies and intelligent diagnostics and forecasting such as cloud computing have significantly affected our lifestyle, observed in many fields, especially healthcare. Also, since the number of new cases of cancer has become very high, there is a need to investigate this matter deeply. Still, there is no systematic review on the application or implementation of the cloud in cancer-care services. Hence, this paper has introduced a comprehensive review of a cloud-centered healthcare system that emphasizes treatment ways in different types of cancer until Sep 2021. The results have shown that the largest study was about the relationship between cancer and the cloud associated with breast cancer. Also, the results have shown that cloud computing facilitates data protection, privacy, and medical record access. Using cloud computing in hospitals, physicians will use advanced programs and tools, and nurses will quickly access patients' information with new Wireless-based technologies. A strong understanding of the practical aspects of cloud computing will help researchers effectively navigate the vast data ecosystems in cancer research. So, by highlighting the advantages and drawbacks of analyzed articles, this study provides a comprehensive and up-to-date report on the field of cloud-based cancer studies to fill the previous gaps.

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A Fuzzy Logic-Based Method for Replica Placement in the Peer to Peer Cloud Using an Optimization Algorithm

Cited by 11 publications

References 79 publications

A novel hybrid arithmetic optimization algorithm and salp swarm algorithm for data placement in cloud computing

A novel hybrid arithmetic optimization algorithm and salp swarm algorithm for data placement in cloud computing

AOEHO: A New Hybrid Data Replication Method in Fog Computing for IoT Application

The Roles of Cloud-Based Systems on the Cancer-Related Studies: A Systematic Literature Review

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