Cloud Computing With Resource Allocation Based on Ant Colony Optimization

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dc.citation.epage110
dc.citation.issue2
dc.citation.spage104
dc.contributor.affiliationLviv Polytechnic National University
dc.contributor.authorKniazhyk, Taras
dc.contributor.authorMuliarevych, Oleksandr
dc.coverage.placenameЛьвів
dc.coverage.placenameLviv
dc.date.accessioned2024-02-19T09:44:33Z
dc.date.available2024-02-19T09:44:33Z
dc.date.created2023-02-28
dc.date.issued2023-02-28
dc.description.abstractIn this study, we explore the intricacies of cloud computing technologies, with an emphasis on the challenges and concerns pertinent to resource allocation. Three optimization 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 elucidating their respective merits and limitations. As the complexity of cloud computing escalates, devising efficacious strategies for resource management and allocation becomes imperative. Such strategies are paramount in aiding organizations in cost containment and performance amplification. The ensuing comparative analysis has been crafted to offer a holistic insight into the three algorithms, thus empowering cloud providers to judiciously select an optimization technique that aligns with the unique demands and challenges of their cloud computing infrastructure.
dc.format.extent104-110
dc.format.pages7
dc.identifier.citationKniazhyk T. Cloud Computing With Resource Allocation Based on Ant Colony Optimization / Taras Kniazhyk, Oleksandr Muliarevych // Advances in Cyber-Physical Systems. — Lviv : Lviv Politechnic Publishing House, 2023. — Vol 8. — No 2. — P. 104–110.
dc.identifier.citationenKniazhyk T. Cloud Computing With Resource Allocation Based on Ant Colony Optimization / Taras Kniazhyk, Oleksandr Muliarevych // Advances in Cyber-Physical Systems. — Lviv : Lviv Politechnic Publishing House, 2023. — Vol 8. — No 2. — P. 104–110.
dc.identifier.doidoi.org/10.23939/acps2023.02.104
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/61337
dc.language.isoen
dc.publisherВидавництво Львівської політехніки
dc.publisherLviv Politechnic Publishing House
dc.relation.ispartofAdvances in Cyber-Physical Systems, 2 (8), 2023
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dc.relation.referencesTsiunyk B., Muliarevych O., (2022). Software System for Motion Detection and Tracking, Advances in Cyber- Physical Systems, 7(2), pp. 156–162. DOI: https://doi.org/10.23939/acps2022.02.156
dc.relation.referencesenDewangan B., Choudhury T., Toe T., Singh B., Nhu N., Tomar R., (2021). Cloud Autonomic Computing in Cloud Resource Management in Industry 4.0. Switzerland: Springer, pp. 123–195. DOI: https://doi=10.1007/978-3-030-71756-8_9
dc.relation.referencesenSehgal N., Bhatt. P., Acken J., (2020). Cloud Computing with Security, Concepts and Practices. Second edition. Switzerland: Springer, pp. 75–109. DOI: https://doi=10.1007/978-3-030-24612-9
dc.relation.referencesenCai J., Peng P., Huang X. and Xu B., (2020). A Hybrid Multi-Phased Particle Swarm Optimization with Sub Swarms, 2020 International Conference on Artificial Intel- ligence and Computer Engineering (ICAICE), Beijing, China, pp. 104–108. DOI: https://doi=10.1109/ICAICE51518.2020.00026
dc.relation.referencesenKozlov O., (2021). Information Technology for Designing Rule bases of Fuzzy Systems using Ant Colony Optimiza- tion, International Journal of Computing, 20(4), pp. 471– 486. DOI: https://doi.org/10.47839/ijc.20.4.2434
dc.relation.referencesenArianyan E., Maleki D., Yari A. and Arianyan I., (2012). Efficient resource allocation in cloud data centers through genetic algorithm, 6th International Symposium on Tele- communications (IST), Tehran, Iran, pp. 566–570. DOI: https://doi=10.1109/ISTEL.2012.6483053
dc.relation.referencesenRaj P., Vanga S., Chaudhary A., (2022). Cloud-native Computing: How to Design, Develop, and Secure Micros- ervices and Event-Driven Applications, John Wiley & Sons, pp. 129–163. DOI: https://doi.org/10.1002/9781119814795.ch13
dc.relation.referencesenSingh A., Indrusiak L., Dziurzanski P., (2022). Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing, e-book, Denmark: River Publishers, pp. 128–154. [Electronic resource], Available at: https://eprints.whiterose.ac.uk/106984/1/Published_ebook_RP_E9788793519077.pdf (Accessed: 01 January 2023)
dc.relation.referencesenKochenderfer M. J., Wheeler T. A., (2019). Algorithms for Optimization. United Kingdom: MIT Press, pp. 125–189. DOI: https://doi.org/10.1109/MCS.2019.2961589
dc.relation.referencesenBadar, Altaf Q. H., (2021). Evolutionary Optimization Algorithms. United States, CRC Press, pp. 113–218. DOI: https://doi.org/10.1201/b22647
dc.relation.referencesenMuliarevych O., (2016). Solving dynamic assymetrical Travelling Salesman Problem in conditions of partly un- known data, Lviv-Slavsk, Lviv Polytechnik Publ., TCSET'2016 vol.1, pp. 446–448. DOI: https://doi.org/10.1109/TCSET.2016. 7452084
dc.relation.referencesenJun S., Yatskiv N., Sachenko A. and Yatskiv V., (2012). Improved method of ant colonies to search independent data transmission routes in WSN, 2012 IEEE 1st Interna- tional Symposium on Wireless Systems (IDAACS-SWS), Offenburg, Germany, pp. 52–57. DOI: https://doi.org/10.1109/IDAACS-SWS.2012.6377632
dc.relation.referenceseniu S., Li Z., (2017). A modified genetic algorithm for community detection in complex networks, 2017 Interna- tional Conference on Algorithms, Methodology, Models and Applications in Emerging Technologies (ICAMMAET), Chennai, India, pp. 1–3. DOI: https://doi.org/10.1109/ICAMMAET.2017.8186747
dc.relation.referencesenYichen L., Bo L., Chenqian Z. and Teng M., (2020). Intel- ligent Frequency Assignment Algorithm Based on Hybrid Genetic Algorithm, 2020 International Conference on Computer Vision, Image and Deep Learning (CVIDL), Chongqing, China, pp. 461–467. DOI: https://doi.org/10.1109/CVIDL51233.2020.00-50
dc.relation.referencesenMuliarevych O., (2022), Acceptance and shipping warehouse zones calculation using serverless approach, 12th Interna- tional Conference on Dependable Systems, Services and Technologies (DESSERT), Athens, Greece, pp. 1–6. DOI: https://doi.org/10.1109/DESSERT58054.2022.10018786
dc.relation.referencesenSampaio A. M. and Barbosa J. G., (2019). Enhancing Reliabil- ity of Compute Environments on Amazon EC2 Spot In- stances, 2019 International Conference on High Performance Computing & Simulation (HPCS), Dublin, Ireland, pp. 708– 715. DOI: https://doi.org/10.1109/HPCS48598.2019.9188116
dc.relation.referencesenEkwe-Ekwe N. and Barker A., (2018). Location, Location, Location: Exploring Amazon EC2 Spot Instance Pricing Across Geographical Regions, 18th IEEE/ACM Interna- tional Symposium on Cluster, Cloud and Grid Computing (CCGRID), Washington, DC, USA, pp. 370–373. DOI: https://doi.org/10.1109/CCGRID.2018.00059
dc.relation.referencesenArdagna D. and Pernici B., (2005). Global and local QoS constraints guarantee in Web service selection, IEEE In- ternational Conference on Web Services (ICWS'05), Or- lando, FL, USA, 2005, pp. 805–806. DOI: https://doi.org/10.1109/ICWS.2005.66
dc.relation.referencesenTsiunyk B., Muliarevych O., (2022). Software System for Motion Detection and Tracking, Advances in Cyber- Physical Systems, 7(2), pp. 156–162. DOI: https://doi.org/10.23939/acps2022.02.156
dc.relation.urihttps://doi=10.1007/978-3-030-71756-8_9
dc.relation.urihttps://doi=10.1007/978-3-030-24612-9
dc.relation.urihttps://doi=10.1109/ICAICE51518.2020.00026
dc.relation.urihttps://doi.org/10.47839/ijc.20.4.2434
dc.relation.urihttps://doi=10.1109/ISTEL.2012.6483053
dc.relation.urihttps://doi.org/10.1002/9781119814795.ch13
dc.relation.urihttps://eprints.whiterose.ac.uk/106984/1/Published_ebook_RP_E9788793519077.pdf
dc.relation.urihttps://doi.org/10.1109/MCS.2019.2961589
dc.relation.urihttps://doi.org/10.1201/b22647
dc.relation.urihttps://doi.org/10.1109/TCSET.2016
dc.relation.urihttps://doi.org/10.1109/IDAACS-SWS.2012.6377632
dc.relation.urihttps://doi.org/10.1109/ICAMMAET.2017.8186747
dc.relation.urihttps://doi.org/10.1109/CVIDL51233.2020.00-50
dc.relation.urihttps://doi.org/10.1109/DESSERT58054.2022.10018786
dc.relation.urihttps://doi.org/10.1109/HPCS48598.2019.9188116
dc.relation.urihttps://doi.org/10.1109/CCGRID.2018.00059
dc.relation.urihttps://doi.org/10.1109/ICWS.2005.66
dc.relation.urihttps://doi.org/10.23939/acps2022.02.156
dc.rights.holder© Національний університет “Львівська політехніка”, 2023
dc.rights.holder© Kniazhyk T., Muliarevych O., 2023
dc.subjectresource allocation
dc.subjectACO
dc.subjectPSA
dc.subjectGA
dc.subjectcloud computing
dc.titleCloud Computing With Resource Allocation Based on Ant Colony Optimization
dc.typeArticle

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Advances In Cyber-Physical Systems. – 2023. – Vol. 8, No. 2