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Journal articles on the topic 'Particle swarm optimization cloud computing'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Zhao, Hong Wei, and Li Wei Tian. "Resource Schedule Algorithm Based on Artificial Fish Swarm in Cloud Computing Environment." Applied Mechanics and Materials 635-637 (September 2014): 1614–17. http://dx.doi.org/10.4028/www.scientific.net/amm.635-637.1614.

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Cloud computing needs to manage a large number of computing resources, while resources scheduling strategy plays a key role in determining the efficiency of cloud computing. evolutionary algorithms (EA) as appropriate tools to optimize multi-objective problems have been applied to optimize Resources Scheduling of cloud computing ,However, studies on improving the convergence ratio and processing time in the most applied algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) in Resources Scheduling domains remain poorly understood. the res

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Reddy, B. Ramana, M. Indiramma, and N. Nagarathna. "Intelligent Particle Swarm Optimization Based Resource Provisioning Technique in Cloud Computing." Indian Journal Of Science And Technology 16, no. 16 (2023): 1241–49. http://dx.doi.org/10.17485/ijst/v16i16.308.

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Shi, Weihang. "QoE Guarantee Scheme Based on Cooperative Cognitive Cloud and Opportunistic Weight Particle Swarm." Journal of Electrical and Computer Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/398709.

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It is well known that the Internet application of cloud services may be affected by the inefficiency of cloud computing and inaccurate evaluation of quality of experience (QoE) seriously. In our paper, based on construction algorithms of cooperative cognitive cloud platform and optimization algorithm of opportunities weight particle swarm clustering, the QoE guarantee mechanism was proposed. The mechanism, through the sending users of requests and the cognitive neighbor users’ cooperation, combined the cooperation of subcloud platforms and constructed the optimal cloud platform with the differ

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Jia, Jia, and Dejun Mu. "Low-Energy-Orientated Resource Scheduling in Cloud Computing by Particle Swarm Optimization." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 36, no. 2 (2018): 339–44. http://dx.doi.org/10.1051/jnwpu/20183620339.

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In order to reduce the energy cost in cloud computing, this paper represents a novel energy-orientated resource scheduling method based on particle swarm optimization. The energy cost model in cloud computing environment is studied first. The optimization of energy cost is then considered as a multiobjective optimization problem, which generates the Pareto optimization set. To solve this multiobjective optimization problem, the particle swarm optimization is involved. The states of one particle consist of both the allocation plan for servers and the frequency plans on servers. Each particle in

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Huang, Mei Geng, and Zhi Qi Ou. "Review of Task Scheduling Algorithm Research in Cloud Computing." Advanced Materials Research 926-930 (May 2014): 3236–39. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.3236.

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The cloud computing task scheduling field representative algorithms was introduced and analyzed : genetic algorithm, particle swarm optimization, ant colony algorithm. Parallelism and global search solution space is the characteristic of genetic algorithm, genetic iterations difficult to proceed when genetic individuals are very similar; Particle swarm optimization in the initial stage is fast, slow convergence speed in the later stage ; Ant colony algorithm optimization ability is good, slow convergence speed in its first stage; Finally, the summary and prospect the future research direction.

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Ramasree, T. "Resource Allocation Using Particle Swarm Optimization Algorithm in Cloud Computing." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (2021): 2679–82. http://dx.doi.org/10.22214/ijraset.2021.37856.

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Abstract: Cloud computing is now widely used in organisations and bussiness firms because of its on-demand accessibility of framework assets, web innovation, and pay-as-you-go principle. Despite of numerous advantages of cloud computing, such as availability and accessibility, it also has few significant drawbacks. The most fundamental issue is the resource management, where Cloud computing provides IT assets such as memory, network, storage, and so on based on a virtualization concept and a pay-as-you-go model. Much research has gone into the administration of these assets. The suggested fram

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Liu, Jing, Xing Guo Luo, Xing Ming Zhang, and Fan Zhang. "Job Scheduling Algorithm for Cloud Computing Based on Particle Swarm Optimization." Advanced Materials Research 662 (February 2013): 957–60. http://dx.doi.org/10.4028/www.scientific.net/amr.662.957.

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Cloud computing is an emerging high performance computing environment with a large scale, heterogeneous collection of autonomous systems and flexible computational architecture. The performance of the scheduling system influences the cost benefit of this computing paradigm. To reduce the energy consumption and improve the profit, a job scheduling model based on the particle swarm optimization(PSO) algorithm is established for cloud computing. Based on open source cloud computing simulation platform CloudSim, compared to GA and random scheduling algorithms, the results show that the proposed al

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Prasanna, Mrs G., Mr R. Siva Sankar Reddy, Mrs B. Sree Harini, and Tadepalli Sreenija. "FUZZY BASED ANT COLONY OPTIMIZATION SCHEDULING IN CLOUD COMPUTING." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 11, no. 2 (2020): 1258–66. http://dx.doi.org/10.61841/turcomat.v11i2.14535.

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Cloud computing is an Information Technology deployment model established on virtualization. Task scheduling states the set of rules for task allocations to an exact virtual machine in the cloud computing environment. However, task scheduling challenges such as optimal task scheduling performance solutions, are addressed in cloud computing. First, the cloud computing performance due to task scheduling is improved by proposing a Dynamic Weighted Round-Robin algorithm. This recommended DWRR algorithm improves the task scheduling performance by considering resource competencies, task priorities,

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Poltronieri, Filippo, Cesare Stefanelli, Mauro Tortonesi, and Mattia Zaccarini. "Reinforcement Learning vs. Computational Intelligence: Comparing Service Management Approaches for the Cloud Continuum." Future Internet 15, no. 11 (2023): 359. http://dx.doi.org/10.3390/fi15110359.

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Modern computing environments, thanks to the advent of enabling technologies such as Multi-access Edge Computing (MEC), effectively represent a Cloud Continuum, a capillary network of computing resources that extend from the Edge of the network to the Cloud, which enables a dynamic and adaptive service fabric. Efficiently coordinating resource allocation, exploitation, and management in the Cloud Continuum represents quite a challenge, which has stimulated researchers to investigate innovative solutions based on smart techniques such as Reinforcement Learning and Computational Intelligence. In

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Wu, Zhou, and Jun Xiong. "A Novel Task-Scheduling Algorithm of Cloud Computing Based on Particle Swarm Optimization." International Journal of Gaming and Computer-Mediated Simulations 13, no. 2 (2021): 1–15. http://dx.doi.org/10.4018/ijgcms.2021040101.

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With the characteristics of low cost, high availability, and scalability, cloud computing has become a high demand platform in the field of information technology. Due to the dynamic and diversity of cloud computing system, the task and resource scheduling has become a challenging issue. This paper proposes a novel task scheduling algorithm of cloud computing based on particle swarm optimization. Firstly, the resource scheduling problem in cloud computing system is modeled, and the objective function of the task execution time is formulated. Then, the modified particle swarm optimization algor

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Ashish, Tripathi Rajnesh Singh Suveg Moudgil Pragati Gupta Nitin Sondhi Tarun Kumar Arun Pratap Srivastava. "A variant of particle swarm optimization in cloud computing environment for scheduling workflow applications." Indonesian Journal of Electrical Engineering and Computer Science 38, no. 2 (2025): 1392–401. https://doi.org/10.11591/ijeecs.v38.i2.pp1392-1401.

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Cloud computing offers on-demand access to shared resources, with user costs based on resource usage and execution time. To attract users, cloud providers need efficient schedulers that minimize these costs. Achieving cost minimization is challenging due to the need to consider both execution and data transfer costs. Existing scheduling techniques often fail to balance these costs effectively. This study proposes a variant of the particle swarm optimization algorithm (VPSO) for scheduling workflow applications in a cloud computing environment. The approach aims to reduce both execution and com

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Xuan Chen, Xuan Chen, and Hongfeng Zheng Xuan Chen. "Research on Task Scheduling Strategy under Mobile Cloud Computing Based on ICSO." 網際網路技術學刊 23, no. 7 (2022): 1483–93. http://dx.doi.org/10.53106/160792642022122307004.

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<p>With the gradual application of mobile terminals such as cell phones in production and life, mobile cloud computing has become an important part of the internet. Different from traditional cloud computing task scheduling methods, mobile cloud computing task scheduling needs to consider not only task time minimization but also the lowest possible mobile device energy consumption. We propose an improved chicken swarm optimization (ICSO) algorithm applied to the task scheduling strategy under mobile cloud computing. First, we establish a multiobjective optimization strategy with minimum

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Ben Alla, Hicham, Said Ben Alla, and Abdellah Ezzati. "A Dynamic Task Scheduling Algorithm for Cloud Computing Environment." Recent Advances in Computer Science and Communications 13, no. 2 (2020): 296–307. http://dx.doi.org/10.2174/2213275911666181018124742.

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Background: Cloud computing environment is a novel paradigm in which the services are hosted, delivered and managed over the internet. Tasks scheduling problem in the cloud has become a very interesting research area. However, the problem is more complex and challenging due to the dynamic nature of cloud and users’ needs as well as cloud providers’ requirements including the quality of service, users’ priorities and computing capabilities. Objective: The main objective is to solve the problem of tasks scheduling through an algorithm which can not only improves the client satisfaction, but also

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Sinaga, Anita Sindar, Sethu Ramen, and Sri Mulyani. "Prediksi Keberhasilan Penanganan Stunting Menggunakan Seleksi Fitur PSO Dengan SaaS Cloud Computing." Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) 23, no. 1 (2024): 87. http://dx.doi.org/10.53513/jis.v23i1.9561.

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Permasalahan stunting merupakan tugas pokok setiap pemerintahan dari perkotaan sampai desa-desa. Deep Learning dapat mengenal pola rumit yan ada pada gambar, dokumen, video, dan data lain untuk menghasilkan prediksi yang akurat. Pengolahan data tidak terstruktur seperti kata, kalimat dapat diekstrak menerapkan Particle Swarm Optimization (PSO). Pengolahan data tidak terstruktur pada kata dan kalimat bersumber dari media online diekstrak menerapkan Particle Swarm Optimization (PSO) mencakup swarm, partikel, Pbest, Gbest, dan Velocity. Melalui empat tahapan algoritma PSO dimulai dari Inisialisas

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Khanna, Palak, Mayank Agarwal, and Khushi Rastogi. "Comparative Analysis of PSO Algorithm in Cloud Computing." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 3347–52. http://dx.doi.org/10.22214/ijraset.2022.43114.

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Abstract: Cloud computing makes it possible to access applications and data from anywhere so this has become new technology. The goals of the paper are to provide additional insights to suggest ways in which performance might be improved by incorporating features from one paradigm into the other. The Reasearch Paper focus on particle swarm optimization (PSO) heuristic-based algorithm based on Scheduling. By scheduling, applications are scheduled to the cloud resources that are used for computation of cost and transmission cost of data in the cloud. by using PSO total cost of execution is minim

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Kumar, Niraj, Upasana Dugal, and Akanksha Singh. "Optimizing Task Scheduling in Cloud Computing Environments using Hybrid Swarm Optimization." Journal of Computers, Mechanical and Management 2, no. 5 (2023): 08–13. http://dx.doi.org/10.57159/gadl.jcmm.2.5.23076.

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Cloud computing has revolutionized the Information Technology (IT) landscape by offering on-demand access to a shared pool of computing resources over the internet. Effective task scheduling is pivotal in optimizing resource utilization and enhancing the overall performance of cloud systems. Tasks are allocated to virtual machines (VMs) based on a server's workload capacity, aiming to minimize traffic congestion and waiting times. Although Particle Swarm Optimization (PSO) is currently the most effective algorithm for task scheduling in cloud environments, this study introduces a Hybrid Swarm

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Tripathi, Ashish, Rajnesh Singh, Suveg Moudgil, et al. "A variant of particle swarm optimization in cloud computing environment for scheduling workflow applications." Indonesian Journal of Electrical Engineering and Computer Science 38, no. 2 (2025): 1392. https://doi.org/10.11591/ijeecs.v38.i2.pp1392-1401.

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<span lang="EN-US">Cloud computing offers on-demand access to shared resources, with user costs based on resource usage and execution time. To attract users, cloud providers need efficient schedulers that minimize these costs. Achieving cost minimization is challenging due to the need to consider both execution and data transfer costs. Existing scheduling techniques often fail to balance these costs effectively. This study proposes a variant of the particle swarm optimization algorithm (VPSO) for scheduling workflow applications in a cloud computing environment. The approach aims to redu

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Zhong, Shao Bo, and Zhong Shi He. "The Scheduling Algorithm of Grid Task Based on PSO and Cloud Model." Key Engineering Materials 439-440 (June 2010): 1487–92. http://dx.doi.org/10.4028/www.scientific.net/kem.439-440.1487.

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Grid task scheduling (GTS) is a NP-hard problem. This paper proposes an optimized GTS algorithm which combines with the advantages of cloud model based on the particle swarm optimization algorithm. This algorithm iterates tasks utilizing the advantages of particle swarm optimization algorithm and then gets a set of candidate solutions quickly. In addition, this algorithm modifies the value of entropy and excess entropy using the characteristics of cloud model and implements the transformation between qualitative variables and quantity of uncertain events. And this algorithm makes particles fly

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He, Dan Dan, Hong Feng Hou, and Li Juan Wang. "Study on Energy-Saving Efficient Resource Scheduling Optimization Algorithm in Cloud Computing." Advanced Materials Research 915-916 (April 2014): 1285–91. http://dx.doi.org/10.4028/www.scientific.net/amr.915-916.1285.

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It is a critical problem that schedules cloud resource in cloud environment. Based on the characteristics of cloud computing and analysis on cloud computing resource scheduling model framework, and the traditional resource scheduling of cloud computing is only concerned the maximum completion time of the task, without taking into account the energy consumption problem, this paper uses an improved particle swarm optimization, that is, when the optimal solution did not change for two generations, traversing through the chaotic particle method for local optimization to accelerate access to global

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Raju, Y. Home Prasanna, and Nagaraju Devarakonda. "A cluster medoid approach for cloud task scheduling." International Journal of Knowledge-based and Intelligent Engineering Systems 25, no. 1 (2021): 65–73. http://dx.doi.org/10.3233/kes-210053.

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One of the familiar distributed technologies for sharing computing resources through internet is a cloud computing technology. One need not setup all computing resources on their own to design their applications. They can own as much they want by requesting computing resources through net. These resources are shared between users upon request by properly scheduling tasks in cloud. The process of scheduling tasks is to be optimized to share the resources very fast. The paper proposes a cluster medoid based task scheduling technique KMPS (K-medoid particle swarm approach) for minimizing the make

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Balicki, Jerzy. "Many-Objective Quantum-Inspired Particle Swarm Optimization Algorithm for Placement of Virtual Machines in Smart Computing Cloud." Entropy 24, no. 1 (2021): 58. http://dx.doi.org/10.3390/e24010058.

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Particle swarm optimization algorithm (PSO) is an effective metaheuristic that can determine Pareto-optimal solutions. We propose an extended PSO by introducing quantum gates in order to ensure the diversity of particle populations that are looking for efficient alternatives. The quality of solutions was verified in the issue of assignment of resources in the computing cloud to improve the live migration of virtual machines. We consider the multi-criteria optimization problem of deep learning-based models embedded into virtual machines. Computing clouds with deep learning agents can support se

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B, Ramana Reddy, Indiramma M, and Nagarathna N. "Intelligent Particle Swarm Optimization Based Resource Provisioning Technique in Cloud Computing." Indian Journal of Science and Technology 16, no. 16 (2023): 1241–49. https://doi.org/10.17485/IJST/v16i16.308.

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Abstract <strong>Objectives:</strong> To find the optimal allocation of resources and minimize the overall cost while meeting the performance requirements of the applications. <strong>Methods:</strong> The proposed Intelligent PSO-based resource optimization in cloud computing evaluates the quality of the solutions based on their resource allocation parameters. Cloud Sim software is used as a simulation tool for testing and evaluating new solutions and strategies in the cloud. The Closest Data Center Service Broker Policy is implemented in Cloud Simulation.<strong> Findings

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Gowda, Suma. "CloudOptimus: AI driven Dynamic Resource Allocation in Cloud Environment using Hybrid Algorithm." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47090.

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Abstract—This paper introduces a new method with a Hybrid Particle Swarm Optimization (HPSO) algorithm to improve resource allocation in cloud computing. The proposed system incorporates adaptive mechanisms and hybridization approaches to enhance convergence rate and solution quality. Extensive simulations prove that the HPSO algorithm performs better than conventional PSO and other heuristic approaches regarding execution time, resource utilization, and Quality of Service (QoS) measures. The results indicate that HPSO provides a strong and scalable solution for dynamic resource allocation in

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Kniazhyk, Taras, and Oleksandr Muliarevych. "Cloud Computing With Resource Allocation Based on Ant Colony Optimization." Advances in Cyber-Physical Systems 8, no. 2 (2023): 104–10. http://dx.doi.org/10.23939/acps2023.02.104.

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In this study, we explore the intricacies of cloud computing technologies, with an emphasis on the challenges and concerns pertinent to resource allocation. Three opti- mization techniques—Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Genetic Algorithm (GA) — have been meticulously analyzed concerning their applications, objectives, and operational methodologies. The study underscores these algorithms' pivotal role in enhancing cloud resource optimization, while also elucidat- ing their respective merits and limitations. As the complexity of cloud computing escalates, d

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Kushwaha, Arvinda, and Mohd Amjad. "A Particle Swarm Optimization Based Load Scheduling Algorithm in Cloud Platform for Wireless Sensor Networks." Scalable Computing: Practice and Experience 20, no. 1 (2019): 71–82. http://dx.doi.org/10.12694/scpe.v20i1.1464.

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Integration of wireless sensor network into cloud computing is a growing paradigm that supports a massive amount of applications in cloud computing, optimization of resources required in the machines. This integration requires the optimization of resources to efficiently complete the different tasks in the devices at cloud platform. This optimization can be done using load scheduling algorithms. These algorithms reduce overload and achieve higher throughput by maximizing the machine utilization concerning cost stabilization. There are lots of methods like First Come First Serve, Min-Min, Parti

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Anbarkhan, Samar Hussni, and Mohamed Ali Rakrouki. "An Enhanced PSO Algorithm for Scheduling Workflow Tasks in Cloud Computing." Electronics 12, no. 12 (2023): 2580. http://dx.doi.org/10.3390/electronics12122580.

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This paper proposes an enhanced Particle Swarm Optimization (PSO) algorithm in order to deal with the issue that the time and cost of the PSO algorithm is quite high when scheduling workflow tasks in a cloud computing environment. To reduce particle dimensions and ensure initial particle quality, intensive tasks are combined when scheduling workflow tasks. Next, the particle initialization is optimized to ensure better initial particle quality and reduced search space. Then, a suitable self-adaptive function is integrated to determine the best direction of the particles. The experiments show t

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Guo, Xing, Shanshan Chen, Yiwen Zhang, and Wei Li. "Service Composition Optimization Method Based on Parallel Particle Swarm Algorithm on Spark." Security and Communication Networks 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/9097616.

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Web service composition is one of the core technologies of realizing service-oriented computing. Web service composition satisfies the requirements of users to form new value-added services by composing existing services. As Cloud Computing develops, the emergence of Web services with different quality yet similar functionality has brought new challenges to service composition optimization problem. How to solve large-scale service composition in the Cloud Computing environment has become an urgent problem. To tackle this issue, this paper proposes a parallel optimization approach based on Spar

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Han, Yan. "Cloud computing task scheduling based on particle swarm optimization algorithm." Procedia Computer Science 261 (2025): 1349–55. https://doi.org/10.1016/j.procs.2025.05.012.

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Zeenath Sultana, Dr. Raafiya Gulmeher, and Asra Sarwath. "Enhanced Particle Swarm Optimization Algorithm for Cloud Computing Environments Workload Scheduling." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 02 (2025): 129–37. https://doi.org/10.47392/irjaeh.2025.0017.

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Because of cloud services' increased accessibility, enhanced performance, and affordability, cloud service providers are always looking for ways to speed up work completion in order to increase revenues and save energy costs. Even though many scheduling algorithms have been developed, many of these methods only focus on one aspect of the scheduling process. An innovative method called the Enhanced Particle Swarm Optimisation Algorithm (EPSOA) is put forward to effectively improve optimisation outcomes for the cloud workload scheduling issue. The PSO and the Lévy flight are integrated by EPSOA.

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Mohanty, Subhadarshini, Prashanta Kumar Patra, Subasish Mohapatra, and Mitrabinda Ray. "MPSO." International Journal of Applied Evolutionary Computation 8, no. 1 (2017): 1–25. http://dx.doi.org/10.4018/ijaec.2017010101.

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Cloud computing is gaining more popularity due to its advantages over conventional computing. It offers utility based services to subscribers on demand basis. Cloud hosts a variety of web applications and provides services on the pay-per-use basis. As the users are increasing in the cloud system, the load balancing has become a critical issue. Scheduling workloads in the cloud environment among various nodes are essential to achieving a better Quality of Service (QOS). It is a prominent area of research as well as challenging to allocate the resources with changeable capacities and functionali

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Mohanty, Subhadarshini, Prashanta Kumar Patra, Mitrabinda Ray, and Subasish Mohapatra. "A Novel Meta-Heuristic Approach for Load Balancing in Cloud Computing." International Journal of Knowledge-Based Organizations 8, no. 1 (2018): 29–49. http://dx.doi.org/10.4018/ijkbo.2018010103.

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Cloud computing is gaining more popularity due to its advantages over conventional computing. It offers utility based services to subscribers on demand basis. Cloud hosts a variety of web applications and provides services on the pay-per-use basis. As the users are increasing in the cloud system, the load balancing has become a critical issue in cloud computing. Scheduling workloads in the cloud environment among various nodes are essential to achieving a better quality of service. Hence it is a prominent area of research as well as challenging to allocate the resources with changeable capacit

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Tanwar, Navdeep, and Dr Praveen Kumar K V. "Review on Machine Learning for Resource Usage Cost Optimization in Cloud Computing." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 468–72. http://dx.doi.org/10.22214/ijraset.2023.51489.

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Abstract: Small and medium-sized enterprises are increasingly adopting cloud computing, and optimizing the cost of cloud resources has become a crucial concern for them. Although several methods have been proposed to optimize cloud computing resources, these methods mainly focus on a single factor, such as compute power, which may not yield satisfactory results in real-world cloud workloads that are multi-factor, dynamic, and irregular. This paper proposes a new approach that utilizes anomaly detection, machine learning, and particle swarm optimization to achieve a cost-optimal cloud resource

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Elmana, Zahraa Tarek Abdelhamed, Magdy Zakria, and Fatma A. Omara. "Pso Optimization algorithm for Task Scheduling on The Cloud Computing Environment." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 13, no. 9 (2014): 4886–97. http://dx.doi.org/10.24297/ijct.v13i9.2389.

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The Cloud computing is a most recent computing paradigm where IT services are provided and delivered over the Internet on demand. The Scheduling problem for cloud computing environment has a lot of awareness as the applications tasks could be mapped to the available resources to achieve better results. One of the main existed algorithms of task scheduling on the available resources on the cloud environment is based on the Particle Swarm Optimization (PSO). According to this PSO algorithm, the applications tasks are allocated to the available resources to minimize the computation cost only. In

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Ali, Syed Muqthadar, N. Kumaran, and G. N. Balaji. "Individual Updating Strategies-based Elephant Herding Optimization Algorithm for Effective Load Balancing in Cloud Environments." International Journal of Computer Network and Information Security 16, no. 2 (2024): 65–78. http://dx.doi.org/10.5815/ijcnis.2024.02.06.

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In this manuscript, an Individual Updating Strategies-based Elephant Herding Optimization Algorithm are proposed to facilitate the effective load balancing (LB) process in cloud computing. Primary goal of proposed Individual Updating Strategies-based Elephant Herding Optimization Algorithm focus on issuing the workloads pertaining to network links by the purpose of preventing over-utilization and under-utilization of the resources. Here, NIUS-EHOA-LB-CE is proposed to exploit the merits of traditional Elephant Herd Optimization algorithm to achieve superior results in all dimensions of cloud c

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Yeh, Wei-Chang, Wenbo Zhu, Ying Yin, and Chia-Ling Huang. "Cloud Computing Considering Both Energy and Time Solved by Two-Objective Simplified Swarm Optimization." Applied Sciences 13, no. 4 (2023): 2077. http://dx.doi.org/10.3390/app13042077.

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Cloud computing is an operation carried out via networks to provide resources and information to end users according to their demands. The job scheduling in cloud computing, which is distributed across numerous resources for large-scale calculation and resolves the value, accessibility, reliability, and capability of cloud computing, is important because of the high development of technology and the many layers of application. An extended and revised study was developed in our last work, titled “Multi Objective Scheduling in Cloud Computing Using Multi-Objective Simplified Swarm Optimization M

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Kollu, Archana, and Sucharita Vadlamudi. "Eagle Strategy with Cauchy Mutation Particle Swarm Optimization for Energy Management in Cloud Computing." International Journal of Intelligent Engineering and Systems 13, no. 6 (2020): 42–51. http://dx.doi.org/10.22266/ijies2020.1231.05.

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Energy management of the cloud datacentre is a challenging task, especially when the cloud server receives a number of the user’s request simultaneously. This requires an efficient method to optimally allocate the resources to the users. Resource allocation in cloud data centers need to be done in optimized manner for conserving energy keeping in view of Service Level Agreement (SLA). We propose, Eagle Strategy (ES) based Modified Particle Swarm Optimization (ES-MPSO) to minimize the energy consumption and SLA violation. The Eagle Strategy method is applied due to its efficient local optimizat

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Gabi, Danlami, Abdul Samad Ismail, Anazida Zainal, and Zalmiyah Zakaria. "Solving Task Scheduling Problem in Cloud Computing Environment Using Orthogonal Taguchi-Cat Algorithm." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 3 (2017): 1489. http://dx.doi.org/10.11591/ijece.v7i3.pp1489-1497.

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In cloud computing datacenter, task execution delay is no longer accidental. In recent times, a number of artificial intelligence scheduling techniques are proposed and applied to reduce task execution delay. In this study, we proposed an algorithm called Orthogonal Taguchi Based-Cat Swarm Optimization (OTB-CSO) to minimize total task execution time. In our proposed algorithm Taguchi Orthogonal approach was incorporated at CSO tracing mode for best task mapping on VMs with minimum execution time. The proposed algorithm was implemented on CloudSim tool and evaluated based on makespan metric. Ex

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Popov, Vladimir. "Particle Swarm Optimization Technique for Task-Resource Scheduling for Robotic Clouds." Applied Mechanics and Materials 565 (June 2014): 243–46. http://dx.doi.org/10.4028/www.scientific.net/amm.565.243.

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The task-resource scheduling problem is one of the fundamental problems for cloud computing. There are a large number of heuristics based approaches to various scheduling workflow applications. In this paper, we consider the problem for robotic clouds. We propose new method of selection of parameters of a particle swarm optimization algorithm for solution of the task-resource scheduling problem for robotic clouds. In particular, for the prediction of values of the inertia weight we consider genetic algorithms, multilayer perceptron networks with gradient learning algorithm, recurrent neural ne

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Goyal, Shanky, Shashi Bhushan, Yogesh Kumar, et al. "An Optimized Framework for Energy-Resource Allocation in a Cloud Environment based on the Whale Optimization Algorithm." Sensors 21, no. 5 (2021): 1583. http://dx.doi.org/10.3390/s21051583.

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Cloud computing offers the services to access, manipulate and configure data online over the web. The cloud term refers to an internet network which is remotely available and accessible at anytime from anywhere. Cloud computing is undoubtedly an innovation as the investment in the real and physical infrastructure is much greater than the cloud technology investment. The present work addresses the issue of power consumption done by cloud infrastructure. As there is a need for algorithms and techniques that can reduce energy consumption and schedule resource for the effectiveness of servers. Loa

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Cheikh, Salmi, and Jessie J. Walker. "Solving Task Scheduling Problem in the Cloud Using a Hybrid Particle Swarm Optimization Approach." International Journal of Applied Metaheuristic Computing 13, no. 1 (2022): 1–25. http://dx.doi.org/10.4018/ijamc.2022010105.

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Synergistic confluence of pervasive sensing, computing, and networking is generating heterogeneous data at unprecedented scale and complexity. Cloud computing has emergered in the last two decades as a unique storage and computing resource to support a diverse assortment of applications. Numerous organizations are migrating to the cloud to store and process their information. When the cloud infrastructures and resources are insufficient to satisfy end-users requests, scheduling mechanisms are required. Task scheduling, especially in a distributed and heterogeneous system is an NP-hard problem

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Naidu, P. Sanyasi, and Babita Bhagat. "Emphasis on Cloud Optimization and Security Gaps: A Literature Review." Cybernetics and Information Technologies 17, no. 3 (2017): 165–85. http://dx.doi.org/10.1515/cait-2017-0037.

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Abstract Cloud computing is emerging as a significant new paradigm in the fields of Service-oriented computing, software engineering, etc. The paper aims to characterize the cloud environment and to study the cloud optimization problems. About 50 papers are collected from the standard journals, and it is first reviewed chronologically to find out the contributions in cloud security. After reviewing, the various challenges addressed in the cloud environment and its performance analysis is discussed. In the next section, the exploration of the meta-heuristic study of cloud optimization is done.

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Attiya, Ibrahim, and Xiaotong Zhang. "A Simplified Particle Swarm Optimization for Job Scheduling in Cloud Computing." International Journal of Computer Applications 163, no. 9 (2017): 34–41. http://dx.doi.org/10.5120/ijca2017913744.

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Sun, Shiyun. "Cloud Computing Resource Scheduling Based on Improved Particle Swarm Optimization Algorithm." Journal of Physics: Conference Series 2023, no. 1 (2021): 012025. http://dx.doi.org/10.1088/1742-6596/2023/1/012025.

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Abstract With the limited resources of Cloud computing (hereinafter referred to as CC), we must improve the quality of scheduling and cost optimization. Therefore, we must organically integrate the resource pool of servers and computers, which will distribute resources dynamically according to the needs of users. Through CC Resource scheduling (hereinafter referred to as RS), we can improve resource utilization, which will greatly reduce the use cost. Through the CC sharing architecture model, we can implement QoS according to different needs of users, which can achieve flexible resource alloc

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Awad, A. I., N. A. El-Hefnawy, and H. M. Abdel_kader. "Enhanced Particle Swarm Optimization for Task Scheduling in Cloud Computing Environments." Procedia Computer Science 65 (2015): 920–29. http://dx.doi.org/10.1016/j.procs.2015.09.064.

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Yarubi, Khadija Salim Al, and S. M. Emdad Hossain. "Dynamic Optimization for Mobile Edge Computing: A Comparative Study." International Journal of Research and Innovation in Applied Science IX, no. II (2024): 34–42. http://dx.doi.org/10.51584/ijrias.2024.90205.

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Edge computing EC is an advanced technology where a server at the edge resource is allocated at the network’s edge, closely to end users, mobile devices, sensors, and the developing Internet of Things (IoT). To date, a large number application using edge computing concept has developed and equipped in the market. However, “Edge” refer to continuum network resources as well as computing; through cloud center of data and the sources of data. The Edge is not limited to request service or contents; it is extended to perform tasks like computing from the cloud. One main reason to get well acceptanc

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Thilagavathy, C. "Leveraging Machine and Deep Learning Models for Load Balancing Strategies in Cloud Computing." Indian Journal Of Science And Technology 17, no. 45 (2024): 4722–31. https://doi.org/10.17485/ijst/v17i45.2728.

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Objectives: To evaluate the efficiency of task prediction and resource allocation for load balancing (LB) in the cloud environment using the combined approach like random Forest(RF) for task prediction and Particle Swarm optimization for optimization and Convolutional Neural Networks (PSO-CNN) for resource prediction and allocation. Methods: The ensemble approach in the present study uses Random Forest (RF), a machine learning (ML) model for task prediction and Particle Swarm Optimization (PSO+CNN), a bio-inspired algorithm and Deep Learning (DL) model for optimization and resource allocation.

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Han, Bo, and Rongli Zhang. "Stochastic Matrix Modelling and Scheduling Algorithm of Distributed Intelligent Computing System." Mathematical Problems in Engineering 2022 (August 28, 2022): 1–11. http://dx.doi.org/10.1155/2022/3730738.

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Parallel and distributed processing has always been a hot field of scientific and technological research, development, and application. It is an important solution in the fields of scientific computing and data service processing, such as weather prediction, wind tunnel Reynolds numerical calculation, and financial services. Intelligent cloud computing has higher requirements for high-capacity and efficient computing. The ability of existing computing system has been difficult to meet its needs. It is necessary to establish an intelligent computing system with the self-organizing ability and r

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Thirumala Rao, B., K. Nandavardhini, K. Navya, and G. Krishna Venkata Sunil. "Improved Virtual Machine Allocation Strategy using Particle Swarm Optimization Algorithm." International Journal of Engineering & Technology 7, no. 2.7 (2018): 813. http://dx.doi.org/10.14419/ijet.v7i2.7.10985.

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Virtual machine position (VMP) is a critical issue in choosing most appropriate arrangement of physical machines (PMs) for an arrangement of virtual machines (VMs) in distributed computing condition. These days information concentrated applications for handling huge information are being facilitated in the cloud. Since the cloud condition gives virtualized assets to calculation, and information concentrated applications require correspondence between the registering hubs, the situation of Virtual Machines (VMs) and area of information influence the general calculation time. The essential targe

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Awotunde, Joseph B., Hrudaya K. Tripathy, and Anjan Bandyopadhyay. "Hybrid Particle Swarm Optimization with Firefly based Resource Provisioning Technique for Data Fusion Fog-Cloud Computing Platforms." Fusion: Practice and Applications 8, no. 2 (2022): 25–35. http://dx.doi.org/10.54216/fpa.080203.

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The recent wide acceptance of cloud and virtualization technologies has made a number of Internet of Things (IoT) applications practical. Although these technologies are typically useful, they may introduce a high transmission latency in IoT environments, e.g., data fusion in smart cities. To address this issue, fog computing, a distributed decentralized computing layer between IoT hardware and the cloud layer, can be used. To facilitate the use of fog computing in IoT data fusion environments, this paper proposes a new Hybrid Particle Swarm Optimization with Firefly based Resource Provisionin

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Li, Xuejun, Jia Xu, and Yun Yang. "A Chaotic Particle Swarm Optimization-Based Heuristic for Market-Oriented Task-Level Scheduling in Cloud Workflow Systems." Computational Intelligence and Neuroscience 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/718689.

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Cloud workflow system is a kind of platform service based on cloud computing. It facilitates the automation of workflow applications. Between cloud workflow system and its counterparts, market-oriented business model is one of the most prominent factors. The optimization of task-level scheduling in cloud workflow system is a hot topic. As the scheduling is a NP problem, Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) have been proposed to optimize the cost. However, they have the characteristic of premature convergence in optimization process and therefore cannot effectivel

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