A metaheuristic‐based data replica placement approach for data‐intensive IoT applications in the fog computing environment

Taghizadeh, Jaber; Ghobaei‐Arani, Mostafa; Shahidinejad, Ali

doi:10.1002/spe.3032

Cited by 13 publications

(7 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a result, the subsequent task scheduling based on data placement information will cause unbalanced task allocation in the system, reducing system throughput. In [19], the authors propose a metaheuristic approach using a non-dominated sorting genetic algorithm II in order to address the latency and traffic differences between different hardware nodes and provide an automated method to manage the transmission of data copies, including their deployment in a fog cloud environment.…”

Section: Related Workmentioning

confidence: 99%

“…However, the neglect of the computing resource state may lead to unbalanced data placement and task allocation in wide-area environments, and reduce the computing efficiency. In recent years, there have been several efforts to accomplish task rescheduling or data redistribution through machine learning and heuristic algorithms, but again, they do not consider the relationship between tasks and data [16][17][18][19]. To summarize, the aforementioned optimization methods mainly focus on optimization through one aspect of task rescheduling or data redistribution rather than on a comprehensive approach, which cannot meet the demands of global performance optimization.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Joint Online Optimization of Task Rescheduling and Data Redistribution

Song¹,

Song²,

Xiao³

et al. 2023

Journal of Internet Technology

View full text Add to dashboard Cite

<p>Wide-area distributed computing environment is the main platform for storing large amounts of data and conducting wide-area computing. Tasks and data are jointly scheduled among multiple computing platforms to improve system efficiency. However, large network latency and limited bandwidth in wide-area networks may cause a large delay in scheduling information and data migration, which brings low task execution efficiency and a long time waiting for data. Traditional works mainly focus on allocating tasks based on data locality or distributing data replications, but optimizing task allocation or data placement alone is insufficient from a global perspective. To mitigate the impact of large network latency and limited bandwidth on system performance, joint online optimization of task rescheduling and data redistribution is proposed in this study. The task allocation and data placement can be adjusted collaboratively during the system running process through the task stealing and backfilling mechanism and the data replication placement mechanism. The experimental results indicate that compared with the state-of-the-art method, the proposed method improves the system throughput and computing resource utilization by 20.67% and 20.26% respectively, and can significantly reduce the global data migration costs.</p> <p> </p>

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Joint Online Optimization of Task Rescheduling and Data Redistribution

Song¹,

Song²,

Xiao³

et al. 2023

Journal of Internet Technology

View full text Add to dashboard Cite

show abstract

“…Cloud computing depends on load balance, bandwidth, and network performance in waiting time, the requirements of users based on the Internet of Things. Response time for users due to delays across nodes in cloud computing requires less bandwidth [9][10][11][12][13][14]. Fog computing extends cloud services to the IoT network's edge, offering numerous benefits such as high performance, reduced response time, bandwidth, and load balancing in fog computing [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

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

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.

show abstract

“…The shortest and least costly route and balancing nodes in IoT applications via fog were also considered. Accessing data across different geographical locations is difficult for the user due to bandwidth, cloud storage, and the response time of users [1][2][3]. Also, transferring data replication through nodes and its proximity to users may need a high cost, which may be the opposite of the users' budget.…”

Section: Introductionmentioning

confidence: 99%

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

Mohamed

Alharbi

Awad

2022

Preprint

View full text Add to dashboard Cite

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.

show abstract

A metaheuristic‐based data replica placement approach for data‐intensive IoT applications in the fog computing environment

Cited by 13 publications

References 33 publications

Joint Online Optimization of Task Rescheduling and Data Redistribution

Joint Online Optimization of Task Rescheduling and Data Redistribution

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

Contact Info

Product

Resources

About