A Data-Placement Strategy Based on Genetic Algorithm in Cloud Computing

Xu, Qiang; Wang, Chao; Wang, Tao

doi:10.4236/ijis.2015.53013

Cited by 26 publications

(6 citation statements)

References 11 publications

(9 reference statements)

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“…Here, high capacity VMs are assigned for high workload reducers. Xu et al [92] proposes a genetic programming approach to optimise the overall number of data transfers. However, this approach does not consider the DCs' capacity constraints and the nonreplication constraints of data sets.…”

Section: Task and Data Scheduling Methodsmentioning

confidence: 99%

A survey on data storage and placement methodologies for Cloud-Big Data ecosystem

et al. 2019

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Over the time, the type of applications has evolved from batch, compute or memory intensive applications to streaming or even interactive applications. As a result, applications are getting more complex and become long-running. Such applications might require frequent-access to multiple distributed data sources. During application deployment and provisioning, the user can face various issues such as (i) where to effectively place both the data and the computation; (ii) how to achieve required objectives while reducing the overall application running cost. Data could be generated from various sources, including a multitude of devices over IoT environments that can generate a huge amount of data, while the applications are running. An application can further produce a large amount of data. In general, data of such size is usually referred to as Big Data. In general, Big Data is characterised by five properties [1, 2]. These are volume, velocity (means rapid update and propagation of data), variety

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Section: Task and Data Scheduling Methodsmentioning

confidence: 99%

A survey on data storage and placement methodologies for Cloud-Big Data ecosystem

et al. 2019

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“…Xu et al introduced a data placement strategy based on a genetic algorithm (DPSBGA) to improve the data placement via datacenters. It reduces the scheduling processes among the datacenters [40].…”

Section: Related Workmentioning

confidence: 99%

A Novel Intelligent Approach for Dynamic Data Replication in Cloud Environment

et al. 2021

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In recent years, cloud computing research, specifically data replication techniques and their applications, has been growing. If the replicas number is raised and put in multiple positions, it will be expensive to maintain the data usability, performance and stability of the application systems. In this paper, two bio-inspired algorithms were proposed to improve both selection and placement of data replicas in the cloud environment. The suggested algorithms for dynamic data replication are multi-objective particle swarm optimization (MO-PSO) and ant colony optimization (MO-ACO). The first suggested algorithm, i.e., MO-PSO, is employed to obtain the best selected data replica depend on the most frequent one. However, the second suggested algorithm, i.e., MO-ACO, is employed to obtain the best data replica placement depend on the shortest distance, and the replicas availability. A simulation of the suggested strategy was carried out using CloudSim. Each data center (DC) includes hosts with set of virtual machines (VMs). The data replication order is determined at random from a thousand cloudlets. All replication files are randomly distributed in the proposed architecture. The performance of suggested techniques was evaluated against several approaches including: Adaptive Replica Dynamic Strategy (ARDS), Enhance Fast Spread (EFS), Genetic Algorithm (GA), Replica Selection and Placement (RSP), Popular File Replication First (PFRF), and Dynamic Cost-aware Re-replication and Re-balancing Strategy (DCR2S). The simulation results prove that MOPSO gives improved data replication compared against other algorithms. Additionally, MOACO realizes higher data availability, lower cost, and less bandwidth consumption compared with other algorithms.

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“…Q. Xu et al proposed a data-placement strategy based on the genetic algorithm (DPSBGA) to schedule data and optimize the placement of data replications via datacenters. It is clear that using the GA provides a better solution for placing and accessing data replications [20].…”

Section: Related Workmentioning

confidence: 99%

A Swarm Intelligence-based Approach for Dynamic Data Replication in a Cloud Environment

Awad¹,

Interpretation²,

Salem³

et al. 2021

IJIES

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In recent years, there has been increasing interest in cloud computing research, especially replication strategies and their applications. When the number of replicas is increased and placed in different places, maintaining the system's data availability, performance and reliability will increase the cost. In this paper, two multi-objectives swarm intelligence algorithms are used to optimize the data replication selection and placement in a cloud environment. These algorithms are namely, multi-objective particle swarm optimization (MOPSO) and multi-objective ant colony optimization (MOACO). The first algorithm, (MOPSO), is used to find the best selected data replica according to the most popular data replication strategy. The improved time-based decay function (ITBDF), is used to enhance the proposed model. The second algorithm, (MOACO), is used to find the best data replica placement according to the minimum distance, the number of data transmissions and the availability of data replication. A simulation of the suggested strategy has been performed using CloudSim. the Cloud is formed to simulate different kinds of datacenters (DCs) with different structures. Moreover, 21 DCs are used. Each DC consists of a host that contains a set of virtual machines (VMs) that provides blocks of available data replications. Three different data placements for high datacenters were created. A total of one thousand cloudlets are randomly confirmed for the data replication order. All replication files are placed in high datacenters and randomly distributed in the suggested system. The performance of proposed strategy was evaluated relative to many well-known strategies such as, Enhance Fast Spread (EFS), Dynamic Cost-aware Re-replication and Re-balancing Strategy (DCR2S), Genetic Algorithm (GA), Genetic adaptive Selection Algorithm (GASA), Replica Selection and Placement (RSP), Dynamic Replica Selection Ant Colony Optimization (DRSACO), Adaptive Replica Dynamic Strategy (ARDS), Popular File Replication First (PFRF). The experimental results show that MOPSO, achieves better data replication than compared algorithms. Additionally, MOACO, achieves higher data availability, lower cost, and less bandwidth consumption than compared algorithms.

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A Data-Placement Strategy Based on Genetic Algorithm in Cloud Computing

Cited by 26 publications

References 11 publications

A survey on data storage and placement methodologies for Cloud-Big Data ecosystem

A survey on data storage and placement methodologies for Cloud-Big Data ecosystem

A Novel Intelligent Approach for Dynamic Data Replication in Cloud Environment

A Swarm Intelligence-based Approach for Dynamic Data Replication in a Cloud Environment

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