Cite this DOI

Ubagaram, C., Mandala, R. R., Garikipati, V., Rao Dyavani, N., Singh Jayaprakasam, B., & Purandhar N (2022). Workload Balancing in Cloud Computing: An Empirical Study on Particle Swarm Optimization, Neural Networks, and Petri Net Models. *Journal of Science & Technology*, *07*(07), 36–57. https://doi.org/10.46243/jst.2022.v7.i07.pp36-57

@article{ubagaram2022workload,
  author    = {Ubagaram, Charles and Mandala, Rohith Reddy and Garikipati, Venkat and Rao Dyavani, Narsing and Singh Jayaprakasam, Bhagath and Purandhar N},
  title     = {{Workload Balancing in Cloud Computing: An Empirical Study on Particle Swarm Optimization, Neural Networks, and Petri Net Models}},
  journal   = {Journal of Science \& Technology},
  year      = {2022},
  month     = {jul},
  volume    = {07},
  number    = {07},
  pages     = {36--57},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jst.2022.v7.i07.pp36-57},
  url       = {https://doi.org/10.46243/jst.2022.v7.i07.pp36-57},
  language  = {en},
  abstract  = {Workload balancing in cloud computing plays a critical role in ensuring efficient task scheduling, resource allocation, and execution management. This study explores the effectiveness of Particle Swarm Optimization (PSO), Neural Networks (NNs), and Petri Net Models (PNMs) for optimizing workload distribution in dynamic cloud environments. The research involves a comparative analysis of these methods, evaluating their computational efficiency, adaptability, and scalability. Mathematical models are developed to formalize their operational principles, and simulations are conducted to measure performance. The findings indicate that a hybrid approach combining PSO, NNs, and PNMs enhances system stability and overall cloud performance, making it a promising solution for intelligent workload balancing}
}

TY  - JOUR
TI  - Workload Balancing in Cloud Computing: An Empirical Study on Particle Swarm Optimization, Neural Networks, and Petri Net Models
AU  - Ubagaram, Charles
AU  - Mandala, Rohith Reddy
AU  - Garikipati, Venkat
AU  - Rao Dyavani, Narsing
AU  - Singh Jayaprakasam, Bhagath
AU  - Purandhar N
JO  - Journal of Science & Technology
PY  - 2022
DA  - 2022/07/23/
VL  - 07
IS  - 07
SP  - 36
EP  - 57
PB  - Longman Publishers
SN  - 2456-5660
LA  - en
AB  - Workload balancing in cloud computing plays a critical role in ensuring efficient task scheduling, resource allocation, and execution management. This study explores the effectiveness of Particle Swarm Optimization (PSO), Neural Networks (NNs), and Petri Net Models (PNMs) for optimizing workload distribution in dynamic cloud environments. The research involves a comparative analysis of these methods, evaluating their computational efficiency, adaptability, and scalability. Mathematical models are developed to formalize their operational principles, and simulations are conducted to measure performance. The findings indicate that a hybrid approach combining PSO, NNs, and PNMs enhances system stability and overall cloud performance, making it a promising solution for intelligent workload balancing
DO  - 10.46243/jst.2022.v7.i07.pp36-57
UR  - https://doi.org/10.46243/jst.2022.v7.i07.pp36-57
ER  -

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    "URL": "https://doi.org/10.46243/jst.2022.v7.i07.pp36-57",
    "title": "Workload Balancing in Cloud Computing: An Empirical Study on Particle Swarm Optimization, Neural Networks, and Petri Net Models",
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    "author": [
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    "abstract": "Workload balancing in cloud computing plays a critical role in ensuring efficient task scheduling, resource allocation, and execution management. This study explores the effectiveness of Particle Swarm Optimization (PSO), Neural Networks (NNs), and Petri Net Models (PNMs) for optimizing workload distribution in dynamic cloud environments. The research involves a comparative analysis of these methods, evaluating their computational efficiency, adaptability, and scalability. Mathematical models are developed to formalize their operational principles, and simulations are conducted to measure performance. The findings indicate that a hybrid approach combining PSO, NNs, and PNMs enhances system stability and overall cloud performance, making it a promising solution for intelligent workload balancing",
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From the record as registered (version 2) — the record and its history. Programs: https://registry.smartscholars.in/api.php?action=cite&doi=10.46243%2Fjst.2022.v7.i07.pp36-57 gives all four in one JSON answer.