An efficient data replica placement mechanism using biogeography-based optimization technique in the fog computing environment

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

doi:10.1007/s12652-021-03495-0

Cited by 14 publications

(2 citation statements)

References 38 publications

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“…They modeled this problem as an integer programming problem and used a swarm intelligence algorithm to solve it. Khatereh Sabaghian et al 35 address the challenge of data placement for IoT devices in fog computing and proposes a metaheuristic‐based data replica placement mechanism using biogeography‐based optimization (BBO). The mechanism aims to reduce latency and data access costs and increase data datasets' reliability and availability while satisfying QoS requirements.…”

Section: Related Workmentioning

confidence: 99%

An effective data placement strategy for IIoT applications

Shen,

Liu,

Dou

2023

Concurrency and Computation

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SummaryWith the rapid development of the Internet of things (IoT) and mobile communication technology, the amount of data related to industrial Internet of things (IIoT) applications has shown a trend of explosive growth, and hence edge‐cloud collaborative environment becomes one of the most popular paradigms to place the IIoT applications data. However, edge servers are often heterogeneous and capacity limited while having lower access delay, so there is a contradiction between capacity and latency while using edge storage. Additionally, when IIoT applications deployed crossing edge regions, the impact of data replication and data privacy should not be ignored. These factors often pose challenges to proposing an effective data placement strategy to take full advantage of edge storage. To address these challenges, an effective data placement strategy for IIoT applications is designed in this article. We first analyze the data access time and data placement cost in an edge‐cloud collaborative environment, with the consideration of data replication and data privacy. Then, we design a data placement strategy based on ‐constraint and Lagrangian relaxation, to reduce the data access time and meanwhile limit the data placement cost to an ideal level. As a result, our proposed data placement strategy can effectively reduce data access time and control data placement costs. Simulation and comparative analysis results have demonstrated the validity of our proposed strategy.

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Section: Related Workmentioning

confidence: 99%

An effective data placement strategy for IIoT applications

Shen,

Liu,

Dou

2023

Concurrency and Computation

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“…Literature [36] also proposed a hybrid meta-heuristic method based on BBO and PSO to estimate the currency demand in Iran. And Recently, Taghizadeh et al [37] proposed a maheuristic-based data replica placement mechanism using BBO for data-intensive IoT applications on the fog ecosystem.…”

Section: Introductionmentioning

confidence: 99%

A Dual Biogeography-Based Optimization Algorithm for Solving High-Dimensional Global Optimization Problems and Engineering Design Problems

2022

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Biogeography-based optimization (BBO) cannot effectively solve high-dimensional global optimization problems due to its single migration mechanism and random mutation operator. To overcome these limitations, this paper propose a dual BBO based on sine cosine algorithm (SCA) and dynamic hybrid mutation, named SCBBO. Firstly, the Latin hypercube sampling method is innovatively used to improve the initial population ergodicity. Secondly, a nonlinear transformation parameter and a inertia weight adjustment factor are designed into the position update formula of SCA to make SCBBO suitable for high dimensional environments. Then, a dynamic hybrid mutation operator is designed by combining Laplacian and Gaussian mutation, which helps the algorithm to escape from local optima and balance the exploration and exploitation. Finally, the dual learning strategy is integrated, so the convergence accuracy is further improved by generating dual individuals. Meanwhile, A sequence convergence model is established to prove the algorithm can converge to the global optimal solution with probability 1. Compared with other state-of-the-art evolutionary algorithms, SCBBO effectively improves the optimization accuracy and convergence speed for high-dimensional optimization problems. To further show the superiority of SCBBO, the performance is compared on 1000, 2000, 5000 and 10000 dimensions, respectively. The comparsions show that SCBBO's optimization results on these dimensions are basically the same. SCBBO also applied to engineering design problems, and the simulation results demonstrate that the proposed method is also effective on constrained optimization problems.

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