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

Awad, Ahmed; Interpretation, Simultaneous; Salem, Rashed; Abdelkader, Hatem; Salam, Mustafa Abdul

doi:10.22266/ijies2021.0430.24

Cited by 8 publications

(7 citation statements)

References 26 publications

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“…The best minimum path and the least expensive option for consumers are considered while placing data replication across DCs. Additionally, it can be shown as [ 28 , 29 , 30 ]:

…”

Section: Suggested System and Discussionmentioning

confidence: 99%

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

Mohamed

Abualigah

Alburaikan

et al. 2023

Sensors

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Recently, the concept of the internet of things and its services has emerged with cloud computing. Cloud computing is a modern technology for dealing with big data to perform specified operations. The cloud addresses the problem of selecting and placing iterations across nodes in fog computing. Previous studies focused on original swarm intelligent and mathematical models; thus, we proposed a novel hybrid method based on two modern metaheuristic algorithms. This paper combined the Aquila Optimizer (AO) algorithm with the elephant herding optimization (EHO) for solving dynamic data replication problems in the fog computing environment. In the proposed method, we present a set of objectives that determine data transmission paths, choose the least cost path, reduce network bottlenecks, bandwidth, balance, and speed data transfer rates between nodes in cloud computing. A hybrid method, AOEHO, addresses the optimal and least expensive path, determines the best replication via cloud computing, and determines optimal nodes to select and place data replication near users. Moreover, we developed a multi-objective optimization based on the proposed AOEHO to decrease the bandwidth and enhance load balancing and cloud throughput. The proposed method is evaluated based on 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 proposed AOEHO strategy performance over other algorithms, such as bandwidth, distance, load balancing, data transmission, and least cost path.

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“…The best minimum path and the least expensive option for consumers are considered while placing data replication across DCs. Additionally, it can be shown as [ 28 , 29 , 30 ]:

…”

Section: Suggested System and Discussionmentioning

confidence: 99%

“…The proposed strategy also provides data to users from different geographical locations according to their budgets. It also determines the most popular files, and the proposed strategy determines and places them in the path of users [ 29 , 30 ].…”

Section: Introductionmentioning

confidence: 99%

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

Mohamed

Abualigah

Alburaikan

et al. 2023

Sensors

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“…( 9) and Eq. ( 10) as follows [33][34][35][36]. We update the velocities for every particle as follows: Accomplishing the mission of reaching the end nodes, the proposed algorithm is proven to choose the optimal nodes to reach the destination by testing the appropriateness of each node according to the agents in the network.…”

Section: A Multi Objective With Particle Swarm Optimizationmentioning

confidence: 99%

A Multi-Objective Optimization for Supply Chain Management using Artificial Intelligence (AI)

Hassouna¹,

El-Henawy²,

Haggag³

2022

IJACSA

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Supply chain management seeks to solve the complex problems of transporting goods from the suppliers to the end customers. Improving the differentiation between different paths to reduce costs and time may require smart systems. This paper proposes two new algorithms for determining, with Multi-Objective Optimization, the least cost and the most appropriate path between two nodes. First: Ant colony optimization (ACO) algorithm, working alongside with Multi Objective Optimization (MOO), is adopted to determine the shortest path and time between two nodes to reach with the least cost. Multi-Objective intelligent Ant Colony (MOIAC) algorithm improves supply chain management to achieve the optimal and the most appropriate solutions. Second: Particle Swarm Optimization (PSO) algorithm, also working alongside MOO, is adopted to determine the least cost, time, and shortest path. Multi Optimization Intelligent Particle Swarm (MOIPS) algorithm improves supply chain management by determining the shortest path with the least cost. These two proposed algorithms seek the optimal solution by MOO using a JAVA Program. The experimental results show the excellence of the first algorithm in determining the optimal and the most appropriate path while getting throw risks inherent in transporting goods. It also demonstrates excellence in transporting goods in the shortest possible time and with the least cost. The second algorithm also shows excellence in transporting goods with the least possible cost via the shortest path and in the shortest time.

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“…Improved genetic algorithms and binary ant colony algorithms are used as metaheuristic tools to optimize multiobjective functions [5]. Awad et al used multiobjective particle swarm optimization (MOPSO) and multiobjective ant colony optimization (MOACO) intelligent algorithms to optimize data replication selection and placement in cloud environments [6]. Yi et al studied the scheduling and collision-free routing problems of AGVs.…”

Section: Related Workmentioning

confidence: 99%

Optimization of Innovation and Entrepreneurship Education and Training System in Colleges and Universities Based on OpenStack Cloud Computing

Cai

2022

Scientific Programming

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With economic globalization and rapid development of science and technology, many colleges and universities pay more and more attention to the cultivation of students’ innovative thinking and creativity, and innovation and entrepreneurship education has also become an important part of the education system. Due to the current unevenness of teachers in innovation and entrepreneurship education in colleges and universities, high training cost, and lack of strong atmosphere, this paper optimizes the innovation and entrepreneurship education and training system in colleges and universities through OpenStack cloud computing. This paper optimizes the cloud computing platform according to the OpenStack virtual machine and the multiobjective ant colony improvement algorithm and then designs the innovation and entrepreneurship education and training system. The multiobjective ant colony improvement algorithm uses the way ants find food to find the best information resource route from the traces left by the information trend in the cloud platform. In order to test the effectiveness of these methods, this paper uses the simulation method to test. The results show that, sometimes, the load utilization rate of the innovation and entrepreneurship education and training system in colleges and universities exceeds 80%, which is in line with the expected settings. Through the OpenStack cloud computing platform, it can provide a good innovation and entrepreneurship training environment for more users at low cost and low risk and promote the development of innovation and entrepreneurship education.

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

Cited by 8 publications

References 26 publications

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

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

A Multi-Objective Optimization for Supply Chain Management using Artificial Intelligence (AI)

Optimization of Innovation and Entrepreneurship Education and Training System in Colleges and Universities Based on OpenStack Cloud Computing

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