Cost-Effective Web Application Replication and Deployment in Multi-Cloud Environment

Shi, Tao; Ma, Hui; Chen, Gang; Hartmann, Sven

doi:10.1109/tpds.2021.3133884

Cited by 26 publications

(9 citation statements)

References 47 publications

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“…MCApp merges iterative mixed integer linear programming with domain-tailored large node search to optimize data replication deployment and user requests. The experiments proved the performance of the proposed strategy using the real and datasets demonstrate that MCApp significantly outperforms other algorithms [34].…”

Section: Related Workmentioning

confidence: 69%

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: 69%

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

et al. 2023

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“…He introduced a new algorithm in file replication called web application replication and deployment in multi-cloud. The proposed algorithm also contributed to reducing users' time, cost and waiting time [18].…”

Section: Tshi Et Almentioning

confidence: 99%

“…𝑃𝐷 𝑖 = 𝑎𝑛 𝑖 * 𝑖 (18) Based on the popularity degree, each file's replication factor (RFi ) is calculated as in Eq. 20.…”

Section: Popularity Degree Of the Data Filementioning

confidence: 99%

A New Hybrid Metaheuristic Algorithm for Energy-Aware Data Replication Based on IoT Applications

Mohamed

Alharbi

Awad

2022

Preprint

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The Internet of Things allows us to connect different devices and objects, generate a large volume of huge data, process it, and store it on cloud computing. Recently, cloud computing has become necessary in using pay-for-use spaces to save time and cost. Used data replication in traditional cloud computing leads to excessive use of resources and devices, performance burdens, increased loads, excessive energy consumption, advanced user waiting, and improved response time. This paper proposes the use of multi-objective optimization (MOO) in combination with harris hawks optimization (HHO) and IoT-based salp swarm algorithm (SSA) in cloud computing. The new method called Multi-objective optimization Harris Hawks Optimization with Salp Swarm Algorithm (MOHHOSSA) is based on a set of objectives and integrating two critical technologies, HHO and SSA, to identify and place data replication across nodes using the least path between nodes in cloud computing. The proposed algorithm improves energy consumption (EC), carbon dioxide emission rate (CDER), and means service time (MST). The laboratory results proved the superiority of the proposed algorithm MOHHOSSA over other algorithms in terms of energy, load balancing, Mean service time, and the least expensive path between nodes in the proposed system.

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“…It considers the average service time of users and the distribution of loads across the network in cloud computing. Suggest an approach called MCA the number of data replicas across nodes with reduced cost and average response time [21].…”

Section: Chen Et Al Introducedmentioning

confidence: 99%

Multi-Objective Optimization for IoT Tasks based Data Replication Placement in Fog Computing

Mohamed

Alburaikan

Khalifa

et al. 2022

Preprint

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IoT applications have recently grown, quickly influencing cloud computing and Artificial Intelligence (AI) applications. Cloud computing has drawn more and more attention, particularly replication techniques and their uses. Cloud computing also provides different spaces according to usage from users and pay-for usage. It also provides flexibility in expanding or reducing the spaces according to use. Data on cloud computing is kept in several locations due to the data's growth and size. It's essential to shorten data transmission times between nodes, reduce bandwidth usage, lighten the pressure on the network, and balance the load across different regions. The expense of maintaining the system's data availability, performance, and reliability will rise as the number of replicas is increased and distributed across more locations. In this paper, we developed an Equilibrium Optimizer (EO) with Multi-Objective Optimization (MOO) to optimize a data replication placement based on IoT on fog computing. Second, we used a levy distribution to distribute a replica between nodes in cloud computing. Also, data transfer via cloud computing with a minimum bandwidth reduction. The performance of the proposed Multi-Objective Equilibrium Optimizer (MOEO) strategy was evaluated with the selection and placement of data replication using different criteria and sizes of data. To confirm the suggested algorithm's outcomes, comparisons were made between it and other algorithms. Experimental results demonstrated the superiority of the proposed algorithm over other algorithms in terms of addressing the problem of data replication placement, improving cost, and reducing data transmission time and distribution across geographically distributed sites.

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Cost-Effective Web Application Replication and Deployment in Multi-Cloud Environment

Cited by 26 publications

References 47 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

A New Hybrid Metaheuristic Algorithm for Energy-Aware Data Replication Based on IoT Applications

Multi-Objective Optimization for IoT Tasks based Data Replication Placement in Fog Computing

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