Un-identical federate replication structure for improving performance of HLA-based simulations

Li, Zengxiang; Cai, Wentong; Turner, Stephen J.

doi:10.1016/j.simpat.2014.06.016

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…The proposed model in [11] introduced a data replication technique to optimize the consumption of resources in edge environments, which reduces the total time for a search request or another procedure, and it shares structures in the device context for analyzing workloads and storing data utilizing mathematical models. The proposed model in [12] improves the high-level architecture performance using a replication method that employs various synchronization approaches for rapid replications.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Replica Selection in Edge and IoT Environments Using Artificial Neural Networks

et al. 2022

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Cloud, edge and Internet of Things (IoT) technologies have emerged to overcome the challenges involved in sharing computational resources and information services. Within generic cloud systems, two models have been identified as having widespread applicability: computation clouds and data clouds. A data cloud is cloud computing that aims to manage, unify and operate multiple data workloads. Many current applications generate datasets consisting of petabytes (PB) of information. Managing large datasets is a complex issuel; in particular, datasets associated with many applications can be distributed widely in geographical terms, particularly in IoT systems. Edge and IoT systems are facing new challenges with increased complexity, making scalability an important issue that will affect the performance of the system. Data replication services are widely accepted techniques to improve availability and fault tolerance, and to improve the data access time. Current replication services, however, often exhibit an increase in response time, reflecting the problems associated with the ever-increasing size of databases. This paper proposes a prediction model to predict replica locations using the files’ access profile, which feeds the neural networks with the access and location behavior (file profile) to minimize the overhead of transferring large volumes of data, which slows down the system and requires careful management. This new model has shown high accuracy and low overheads. The result shows a significant improvement in total task execution time using the proposed model for locating files by 16.34% and 30.45%; in addition, the results show bandwidth improvement by 24.7% and 49.4% compared to the user profile prediction model and replica service model without prediction, respectively. Consequently, the proposed algorithm can improve data access speed, reduce data access latency and decrease bandwidth consumption.

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Section: Related Workmentioning

confidence: 99%

Intelligent Replica Selection in Edge and IoT Environments Using Artificial Neural Networks

et al. 2022

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“…Different replications are developed in the underlying approach. The proposed structure employed various synchronization approaches for rapid replications [41].…”

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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Three-level-parallelization support framework for large-scale analytic simulation

Yao

Dong

Zhu

et al. 2017

Journal of Simulation

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Un-identical federate replication structure for improving performance of HLA-based simulations

Cited by 4 publications

References 21 publications

Intelligent Replica Selection in Edge and IoT Environments Using Artificial Neural Networks

Intelligent Replica Selection in Edge and IoT Environments Using Artificial Neural Networks

A Novel Intelligent Approach for Dynamic Data Replication in Cloud Environment

Three-level-parallelization support framework for large-scale analytic simulation

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