# AOEHO: A New Hybrid Data Replication Method in Fog Computing for IoT Application

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## Abstract

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## 1. Introduction

#### Motivation and Contributions

- Design a discrete AOEHO strategy for solving the dynamic data replication problem in a fog computing environment.
- Improving a swarm intelligent technique based on the hybrid aquila optimizer (AO) algorithm with the elephant herding optimization (EHO) for solving dynamic data replication problems in the fog computing environment.
- Developing a multi-objective optimization based on the proposed AOEHO to decrease the bandwidth to enhance the load balancing and cloud throughput. It evaluates data replication using seven criteria. These criteria are data replication access, distance, costs, availability, SBER, popularity, and the Floyd algorithm.
- The experimental results show the superiority of the AOEHO strategy performance over other algorithms, such as bandwidth, distance, load balancing, data transmission, and least cost path.

## 2. Related Work

## 3. Suggested System and Discussion

#### 3.1. Proposed System and Structure

#### 3.2. Aquila Optimizer (AO)

- Step 1: Expanded exploration

- Step 2: Narrowed exploration

- Step 3: Expanded exploitation

- Step 4: Narrowed exploitation

#### 3.3. Elephant Herding Optimization

#### 3.3.1. Clan-Updating Operator

_{new}, ci,j and x

_{ci,j}present the new and old positions for elephant j in clan ci. x

_{best,ci}is matriarch, representing the clan’s best elephant. a is in the range [0, 1], and r is in the range [0, 1]. The best elephant can be represented as follows:

_{ci}indicates the number of elephants in clan ci.

#### 3.3.2. Separating Operator

#### 3.4. Proposed Swarm Intelligence for Data Replication

#### 3.4.1. Cost and Time of Replication

#### 3.4.2. Shortest Paths Problem (SPP) between Nodes Based on the Floyd

#### 3.4.3. Popularity Degree of the Data File

_{i}) is calculated based on the popularity degree as in Equation (25).

#### 3.4.4. System-Level Availability

#### 3.4.5. Placement of New Replicas

#### 3.5. Computational Complexity

Algorithm 1: The Proposed Algorithm AOEHO |

Input: Regions, datacenters, data availability, minimum distance between regions, cost, time, SBER, fog nodes, popularity data file, max_iter, population size, and number of IoT tasks. Output: select and place data file replica optimalBeginInitialize no. of IoT tasks Initialize the population Initialize the population using the fitness function Initialize availability and unavailability probabilities Initialize replicas according to costs and time Initialize distance between regions Initialize popularity data file Initialize data replication costs and time Initialize optimal best data replica placement in DC solution Initialize the least cost path Initialize SBER Initialize RF Initialize budget repeatInitialization phase:Initialize the population X of the AO.Initialize the parameters of the AO.WHILE (t < T)Calculate the fitness function values.Determine the best-obtained solution according to the fitness values (Xbest(t)).FOR (i = 1,2,...,N)Update the mean value of the current solution XM(t).Update the x, y, G1, G2, Levy(D), etc.IF (t ≤ (2/3)*T)IF (rand ≤ 0.5)Update the current solution using Equation (1). Step 1: Expanded exploration (X1) IF (Fitness X1(t + 1) < Fitness X(t))X(t) = X1(t + 1) IF (Fitness X1(t + 1) <Fitness (Xbest(t))Xbest(t) = X1(t + 1) ENDIFENDIFELSE Update the current solution using Equation (3). Step 2: Narrowed exploration (X2) IF (Fitness X2(t + 1) <Fitness X(t))X(t) = X1(t + 1) IF (Fitness X2(t + 1) <Fitness (Xbest(t))Xbest(t) = X2(t + 1) ENDIFENDIFENDIF ELSE IF (rand ≤ 0.5)Update the current solution using Equation (11). Step 3: Expanded exploitation (X3) IF (Fitness X3(t + 1) <Fitness X(t))X(t) = X3(t + 1) IF (Fitness X3(t + 1) <Fitness (Xbest(t))Xbest(t) = X3(t + 1) ENDIFENDIFELSE Update the current solution using Equation (12). Step 4: Narrowed exploitation (X4) IF (Fitness X4(t + 1) <Fitness X(t))X(t) = X4(t + 1) IF (Fitness X4(t + 1) <Fitness (Xbest(t))Xbest(t) = X4(t + 1) ENDIFENDIFENDIFENDIFENDFORENDWHILEReturn the best solution (Xbest).Apply Elephant Herding Optimization (EHO) to AO t++ End while Calculate the RF Calculate the distance between regions Calculate SBER Calculate the cost and time Return the optimal minimum data replica placement in the region. |

## 4. Experimental Evaluation

#### 4.1. Configuration Details

#### 4.2. Results and Discussion

#### 4.2.1. Different Scenarios of Data Replica Size

#### First Scenario of Tasks

#### Second Scenario of Response Time for Tasks

#### Third Scenario of Response Time for File

#### Third Scenario of Execution Time

#### Fourth Scenario of Data Transmission in Nodes

#### Fifth Scenario of Data Transmission in Tasks

#### 4.3. Performance Evaluation

#### 4.3.1. Degree of Balancing

#### 4.3.2. Data Loss Rate

#### 4.3.3. Load Balancing

#### 4.3.4. Throughput Time

## 5. Conclusions and Future Work

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 2.**Cost number of tasks and cost = 100 tasks. (

**a**) Size of data replication = 64 MB. (

**b**) Size of data replication = 320 MB.

**Figure 3.**Cost number of tasks and cost = 1000 tasks. (

**a**) Size of data replication = 64 MB. (

**b**) Size of data replication = 320 MB.

**Figure 4.**Cost number of tasks and cost = 5000 tasks. (

**a**) Size of data replication = 64 MB. (

**b**) Size of data replication = 320 MB.

**Figure 5.**Response time of tasks = 5000 tasks. (

**a**) Size of data replication = 64 MB. (

**b**) Size of data replication = 320 MB.

**Figure 6.**Response time of data file. (

**a**) Size of data replication = 320 MB. (

**b**) Size of data replication = 320 MB.

**Figure 7.**Execution time of data file. (

**a**) Size of data replication = 64 MB. (

**b**) Size of data replication = 320 MB.

**Figure 8.**Data transmission between fog nodes = 25 nodes. (

**a**) Size of data replication = 64 MB. (

**b**) Size of data replication = 320 MB.

**Figure 9.**Data transmission between fog nodes = 50 nodes. (

**a**) Size of data replication = 64 MB. (

**b**) Size of data replication = 320 MB.

**Figure 10.**Data transmission between fog nodes = 100 nodes. (

**a**) Size of data replication = 64 MB. (

**b**) Size of data replication = 320 MB.

**Figure 11.**Data transmission between tasks = 1000 tasks. (

**a**) Size of data replication = 64 MB. (

**b**) Size of data replication = 320 MB.

**Figure 12.**Data transmission between tasks = 2500 tasks. (

**a**) Size of data replication = 64 MB. (

**b**) Size of data replication = 320 MB.

**Figure 13.**Data transmission between tasks = 5000 tasks. (

**a**) Size of data replication = 64 MB. (

**b**) Size of data replication = 320 MB.

Author | Advantage | Disadvantage |
---|---|---|

K. Sarwar et al., in [31] | Privacy | High latency |

Secrecy | High bandwidth | |

Reliability | load balancing | |

Authentication | ||

D. Chenet al., in [32] | Reliability | High replication cost |

Secrecy | High response time | |

Privacy | ||

C. Li et al., in [33] | load balancing | High response time |

storage | High cost | |

data transmission time | ||

T. Shiet et al., in [34] | High availability | High cost |

High performance | High response time | |

A. Majed et al., in [35] | decreased user waiting | High cost |

load balancing | High storage cost | |

data transmission time | ||

C. LiA et al., in [36] | data transmission time | High response time |

load balancing | ||

low cost | ||

A. Khelifa et al., in [37] | Low response time | High cost |

data transmission time | High storage cost | |

load balancing | ||

B. Mohammadi et al., in [38] | Low response time | High cost |

load balancing | High storage cost |

Cloud Entity | Ranges |
---|---|

Nodes | [1, 120] |

User | [10, 1000] |

Regions | [5, 50] |

Geographical | [10, 64] |

Bandwidth | [2 Mbps, 256 Mbps] |

Data sets | [0.1, 64 G] |

Data file | [10, 1000] |

Cost of file | [100, 5000] |

Storage nodes | [8, 512] |

Transfer rate | [16, 256 MB/s] |

Host | [10, 300] |

Processor | [12, 128] |

MIPS | [100, 20,000] |

Memory RAM | [2, 64 G] |

Virtual machine | [100, 1000] |

Processor | [8, 128] |

MIPS | [200, 20,000] |

Memory RAM | [2, 64 G] |

Cloudlet | [1000, 6000] |

Length of task | [1000, 10,000 MI] |

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## Share and Cite

**MDPI and ACS Style**

Mohamed, A.a.; Abualigah, L.; Alburaikan, A.; Khalifa, H.A.E.-W.
AOEHO: A New Hybrid Data Replication Method in Fog Computing for IoT Application. *Sensors* **2023**, *23*, 2189.
https://doi.org/10.3390/s23042189

**AMA Style**

Mohamed Aa, Abualigah L, Alburaikan A, Khalifa HAE-W.
AOEHO: A New Hybrid Data Replication Method in Fog Computing for IoT Application. *Sensors*. 2023; 23(4):2189.
https://doi.org/10.3390/s23042189

**Chicago/Turabian Style**

Mohamed, Ahmed awad, Laith Abualigah, Alhanouf Alburaikan, and Hamiden Abd El-Wahed Khalifa.
2023. "AOEHO: A New Hybrid Data Replication Method in Fog Computing for IoT Application" *Sensors* 23, no. 4: 2189.
https://doi.org/10.3390/s23042189