A Novel Intelligent Approach for Dynamic Data Replication in Cloud Environment

Awad, Ahmed; Salem, Rashed; Abdelkader, Hatem; Salam, Mustafa Abdul

doi:10.1109/access.2021.3064917

Cited by 16 publications

(8 citation statements)

References 63 publications

(53 reference statements)

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“…Recent research has introduced a bio-based Multi-Objective Particle Swarm Optimization (MO-PSO) and Ant Colony Optimization (MO-ACO). The researcher developed a novel intelligent approach for dynamic data replication in a cloud environment [27]. The first MO-PSO to select replica depends on the most requested by users.…”

Section: Data Replications With Data Center Selection Methodsmentioning

confidence: 99%

“…In data replication environments, there are two (2) common mechanisms for replication strategies. First of all, static replication is a predefined strategy for particular replica environments and is very easy to implement, but this strategy typically does not adapt to every environment [26,27]. The second mechanism is dynamic replication, known as agile replication strategies, where the algorithm can efficiently create and remove any replicas depending on the access trends of system users [19,28].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Replication Strategy with Comprehensive Data Center Selection Method in燙loud Environments

Fazlina¹,

Latip²,

Ibrahim³

et al. 2023

Computers, Materials &Amp; Continua

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As the amount of data continues to grow rapidly, the variety of data produced by applications is becoming more affluent than ever. Cloud computing is the best technology evolving today to provide multi-services for the mass and variety of data. The cloud computing features are capable of processing, managing, and storing all sorts of data. Although data is stored in many high-end nodes, either in the same data centers or across many data centers in cloud, performance issues are still inevitable. The cloud replication strategy is one of best solutions to address risk of performance degradation in the cloud environment. The real challenge here is developing the right data replication strategy with minimal data movement that guarantees efficient network usage, low fault tolerance, and minimal replication frequency. The key problem discussed in this research is inefficient network usage discovered during selecting a suitable data center to store replica copies induced by inadequate data center selection criteria. Hence, to mitigate the issue, we proposed Replication Strategy with a comprehensive Data Center Selection Method (RS-DCSM), which can determine the appropriate data center to place replicas by considering three key factors: Popularity, space availability, and centrality. The proposed RS-DCSM was simulated using CloudSim and the results proved that data movement between data centers is significantly reduced by 14% reduction in overall replication frequency and 20% decrement in network usage, which outperformed the current replication strategy, known as Dynamic Popularity aware Replication Strategy (DPRS) algorithm.

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Section: Data Replications With Data Center Selection Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Replication Strategy with Comprehensive Data Center Selection Method in燙loud Environments

Fazlina¹,

Latip²,

Ibrahim³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…1) Cost: Costs among different nodes are calculated to reach the optimal cost. Costs of transporting goods from suppliers and customers must be low for the variant means of transportation [34]. We need to consider that the system is a Heterogeneous system.…”

Section: A Multi-objective Optimization In Supply Chainmentioning

confidence: 99%

“…( 9) and Eq. ( 10) as follows [33][34][35][36]. We update the velocities for every particle as follows: Accomplishing the mission of reaching the end nodes, the proposed algorithm is proven to choose the optimal nodes to reach the destination by testing the appropriateness of each node according to the agents in the network.…”

Section: A Multi Objective With Particle Swarm Optimizationmentioning

confidence: 99%

A Multi-Objective Optimization for Supply Chain Management using Artificial Intelligence (AI)

Hassouna¹,

El-Henawy²,

Haggag³

2022

IJACSA

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Supply chain management seeks to solve the complex problems of transporting goods from the suppliers to the end customers. Improving the differentiation between different paths to reduce costs and time may require smart systems. This paper proposes two new algorithms for determining, with Multi-Objective Optimization, the least cost and the most appropriate path between two nodes. First: Ant colony optimization (ACO) algorithm, working alongside with Multi Objective Optimization (MOO), is adopted to determine the shortest path and time between two nodes to reach with the least cost. Multi-Objective intelligent Ant Colony (MOIAC) algorithm improves supply chain management to achieve the optimal and the most appropriate solutions. Second: Particle Swarm Optimization (PSO) algorithm, also working alongside MOO, is adopted to determine the least cost, time, and shortest path. Multi Optimization Intelligent Particle Swarm (MOIPS) algorithm improves supply chain management by determining the shortest path with the least cost. These two proposed algorithms seek the optimal solution by MOO using a JAVA Program. The experimental results show the excellence of the first algorithm in determining the optimal and the most appropriate path while getting throw risks inherent in transporting goods. It also demonstrates excellence in transporting goods in the shortest possible time and with the least cost. The second algorithm also shows excellence in transporting goods with the least possible cost via the shortest path and in the shortest time.

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“…and 3) where the new replicas should be placed? These three main open questions must be tackled for data replication in the cloud environments [28][29][30].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

Self Cite

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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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A Novel Intelligent Approach for Dynamic Data Replication in Cloud Environment

Cited by 16 publications

References 63 publications

Replication Strategy with Comprehensive Data Center Selection Method in燙loud Environments

Replication Strategy with Comprehensive Data Center Selection Method in燙loud Environments

A Multi-Objective Optimization for Supply Chain Management using Artificial Intelligence (AI)

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

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