Cloud Service Resource Allocation with Particle Swarm Optimization Algorithm

Cheng, Shi; Guo, Lanhui; Yang, Tao; Feng, Jun; Sun, Yifei; Shao, Cheng; Duan, Qiqi

doi:10.1007/978-981-10-7179-9_41

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…According to the work of Cheng et al, the authors have proposed a particle swarm optimization (PSO) algorithm for evaluating the resource scheduling and allocation approaches in cloud computing. Delay and prices factors as the QoS metrics have been evaluated based on minimizing the objective functions.…”

Section: Related Workmentioning

confidence: 99%

An efficient task scheduling approach using moth‐flame optimization algorithm for cyber‐physical system applications in fog computing

Ghobaei‐Arani

Souri

Safara

et al. 2019

Trans Emerging Tel Tech

126

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With the rapid development of in modern technologies has led to a novel generation of cyberspace equipment and secure analysis methods such as cyber‐physical system (CPS). The CPS system refers to an intelligent dependable system of embedded smart devices used to monitor and control the cyberspace conditions in a fog computing environment. The fog computing environment connects resources even human beings together to enhance the quality of life through performing of CPS applications using computation resources at the network edge. The optimization of task scheduling for the CSP applications as a challenging issue has been considered as the NP‐hard problem in fog computing environment which is not trivial. On the one hand, reasonable tasks scheduling can increase resource utilization, which can avoid less idle resource. This paper presents a task scheduling algorithm based on moth‐flame optimization algorithm to assign an optimal set of tasks to fog nodes to meet the satisfaction of quality of service requirements of CPS applications in such a way that the total execution time of tasks is minimized. The minimization of task execution and transfer time in the proposed algorithm are considered as objective functions. The experimental testing of the proposed solution is carried out in iFogSim toolkit. As a result of the simulation based on proposed algorithm found, the optimal solution for the scheduling of tasks and equal distribution of tasks to fog nodes has been provided, and less total execution time consumption has been achieved compared with other algorithms.

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Section: Related Workmentioning

confidence: 99%

An efficient task scheduling approach using moth‐flame optimization algorithm for cyber‐physical system applications in fog computing

Ghobaei‐Arani

Souri

Safara

et al. 2019

Trans Emerging Tel Tech

126

View full text Add to dashboard Cite

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“…Resource allocation is the process of allocating and managing assets in an optimized way to support the strategic objectives of an organization. SI algorithms have been used in many related applications such as Cloud service resource allocation [ 8 ], wireless network planning [ 2 ], etc.…”

Section: Real-world Applicationsmentioning

confidence: 99%

Swarm Intelligence in Data Science: Applications, Opportunities and Challenges

Yang

Shen

et al. 2020

Lecture Notes in Computer Science

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The Swarm Intelligence (SI) algorithms have been proved to be a comprehensive method to solve complex optimization problems by simulating the emergence behaviors of biological swarms. Nowadays, data science is getting more and more attention, which needs quick management and analysis of massive data. Most traditional methods can only be applied to continuous and differentiable functions. As a set of population-based approaches, it is proven by some recent research works that the SI algorithms have great potential for relevant tasks in this field. In order to gather better insight into the utilization of these methods in data science and to provide a further reference for future researches, this paper focuses on the relationship between data science and swarm intelligence. After introducing the mainstream swarm intelligence algorithms and their common characteristics, both the theoretical and real-world applications in the literature which utilize the swarm intelligence to the related domains of data analytics are reviewed. Based on the summary of the existing works, this paper also analyzes the opportunities and challenges in this field, which attempts to shed some light on designing more effective algorithms to solve the problems in data science for real-world applications.

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A Generic Big Data Analytics With Particle Swarm Optimization for Clinical Machine Learning

Subramani,

Chelladurai,

Chelladurai

et al. 2023

Advances in Healthcare Information Systems and Administration

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In the past two decades, researchers are facing challenges in analyzing biomedical data. It is a fast growing and new promising field due to vast growing information. Data analytics and machine learning are possible on huge volume of biomedical data. The primary goal of the biomedical data analysis by using swarm intelligence will help you to provide optimized prediction or analysis to the problem or diseases. Swarm intelligence is a special kind of information exchange across different nodes in a network with methods in machine learning. All the members of the swarm have equal rights. So there is no spider controlling the data web. Particle swarm optimization is used to fix the parameters and predicts the efficient nodes to pass on. Particle swarm optimization convolution neural networks (PSOCNN) minimizes the parameter space to a single block and evaluates the candidate CNN with a small subset of training data.

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Cloud Service Resource Allocation with Particle Swarm Optimization Algorithm

Cited by 3 publications

References 15 publications

An efficient task scheduling approach using moth‐flame optimization algorithm for cyber‐physical system applications in fog computing

An efficient task scheduling approach using moth‐flame optimization algorithm for cyber‐physical system applications in fog computing

Swarm Intelligence in Data Science: Applications, Opportunities and Challenges

A Generic Big Data Analytics With Particle Swarm Optimization for Clinical Machine Learning

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